Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes’ physics and movement, the implicit semantics in fashion designs, and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this article, we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine--crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may coexist. We discuss how this leads to a novel class of crowd-powered systems for which the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how this kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end user.

This paper focuses on detecting and localizing anomalous events in videos of crowded scenes, i.e., divergences from a dominant pattern. Both motion and appearance information are considered, so as to robustly distinguish different kinds of anomalies, for a wide range of scenarios. A newly introduced concept based on swarm theory, histograms of oriented swarms (HOS), is applied to capture the dynamics of crowded environments. HOS, together with the well-known histograms of oriented gradients, are combined to build a descriptor that effectively characterizes each scene. These appearance and motion features are only extracted within spatiotemporal volumes of moving pixels to ensure robustness to local noise, increase accuracy in the detection of local, nondominant anomalies, and achieve a lower computational cost. Experiments on benchmark data sets containing various situations with human crowds, as well as on traffic data, led to results that surpassed the current state of the art (SoA), confirming the method's efficacy and generality. Finally, the experiments show that our approach achieves significantly higher accuracy, especially for pixel-level event detection compared to SoA methods, at a low computational cost.

In this work we propose a novel approach to the detection of anomalous events occurring in crowded scenes. Swarm theory is applied for the creation of a motion feature first introduced in this work, the Histograms of Oriented Swarm Accelerations (HOSA), which are shown to effectively capture a scene's motion dynamics. The HOSA, together with the well known Histograms of Oriented Gradients (HOGs) describing appearance, are combined to provide a final descriptor based on both motion and appearance, to effectively characterize a crowded scene. Appearance and motion features are only extracted within spatiotemporal volumes of moving pixels (regions of interest) to ensure robustness to local noise and allow the detection of anomalies occurring only in a small region of the frame. Experiments and comparisons with the State of the Art (SoA) on a variety of benchmark datasets demonstrate the effectiveness of the proposed method, its flexibility and applicability to different crowd environments, and its superiority over currently existing approaches.

The automated analysis of crowd behavior from videos has been a rather challenging problem to address due to the complexity and density of the motion, occlusions and local noise. A novel approach for the fast and reliable detection and characterization of abnormal events in crowd motions is proposed, based on particle advection and accurate optical flow estimation. Experiments on benchmark datasets show that changes are detected reliably and faster than existing methods. Also, regions of change are localized spatially, and the events occurring in the video are characterized with accuracy.

This paper presents two algorithms for estimating depth from integral images, which capture a scene by using multiple lenses, offering anaglyph depictions. The first algorithm involves the 3-D integral imaging grid formed by casting rays inversely through the lenses used to capture the integral image. In this formulation, depth estimation is equivalent to finding correspondences on the ray-crossing points. The second algorithm follows the depth-through disparity approach. In this case, a stereo-like minimization problem is formulated which is handled by the graph cuts method. The novelty of the proposed paper lies in constraining the optimization procedures with the “anchor points”. This results in enhanced estimation accuracy, while eliminating the optimization complexity. Anchor points is a set of reliable reference points, detected by applying a robust local image descriptor to viewpoint images, called self-similarity descriptor. The performance of both algorithms is evaluated on a synthetic integral image database in comparison with another state-of-the-art algorithm.

In this paper, a probabilistic approach to combining spatial context with visual and co-occurrence information for semantic image analysis is presented. Overall, the examined image is segmented and subsequently an initial classification of the resulting image regions to semantic concepts is performed based solely on visual information. Then, a Genetic Algorithm (GA) is introduced for deciding on the optimal semantic image interpretation, realizing image analysis as a global optimization problem. The fundamental novelty of this work is that the GA incorporates in its evolutionary procedure a set of Bayesian Networks (BNs), which probabilistically learn the impact of the available spatial, visual and co-occurrence information on the final outcome for every possible pair of semantic concepts. Experimental results on two publicly available datasets demonstrate the efficiency of the proposed approach.

In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key parts of the authors' previous work on spatial context utilization, where a Genetic Algorithm (GA) was introduced for exploiting fuzzy directional relations after performing an initial classification of image regions to semantic concepts using solely visual information. In the extensions reported in this work, a more elaborate approach is followed during the spatial knowledge acquisition and modeling process. Additionally, the impact of every resulting spatial constraint on the final outcome is adaptively adjusted. Experimental results as well as comparative evaluation on three datasets of varying complexity in terms of the total number of supported semantic concepts demonstrate the efficiency of the proposed method.

Content searching and downloading are the two dominant actions of the Internet users today, despite the fact that the Internet was not originally architected to serve such actions. Content Centric Networking is the new trend in the research community to build network architectures that route content by name and not by the host's network address so as to efficiently deal with content persistence, availability and authenticity issues. In this work we propose an extension to the Content Centric Network protocol in order to support content search as a native process of the network. By using object descriptors to represent the actual content objects and integrate these descriptors in the network protocol we manage to search and retrieve content from the network not only by name but also by the content itself. In this approach, searching for information is a process distributed to the reachable network. Moreover, content aggregation is handled by the end user and not by content aggregation portals, thematic content search engines or information curators. By doing so, search is not any more an application but part of the network and can pave the way for many novel applications which have never been thought until today.

Abstract In this paper we present a new method for improving the performance of the widely used Bounding Volume Hierarchies for collision detection. The major contribution of our work is a culling algorithm that serves as a generalization of the Separating Axis Theorem for non parallel axes, based on the well‐known concept of support planes. We also provide a rigorous definition of support plane mappings and implementation details regarding the application of the proposed method to commonly used bounding volumes. The paper describes the theoretical foundation and an overall evaluation of the proposed algorithm. It demonstrates its high culling efficiency and in its application, significant improvement of timing performance with different types of bounding volumes and support plane mappings for rigid body simulations.

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

This paper presents a novel framework for partial matching and retrieval of 3-D models based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3-D target model. The concept behind the proposed algorithm is that, for a 3-D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the salient features of the objects, when compared to other parameter sets or other target 3-D models. In the context of the developed framework, a novel method is also proposed to hierarchically search in the parameter space for the optimum solution. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion.

In this paper, a graphical modeling-based approach to semantic video analysis is presented for jointly realizing modality fusion and temporal context exploitation. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for every modality. Subsequently, an integrated Bayesian Network (BN) is introduced for simultaneously performing information fusion and temporal contextual knowledge exploitation, contrary to the usual practice of performing each task separately. The final outcome of the overall video analysis approach is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach in the domain of news broadcast video are presented.

In this paper, an approach to semantic video analysis that is based on the statistical processing and representation of the motion signal is presented. Overall, the examined video is temporally segmented into shots and for every resulting shot appropriate motion features are extracted; using these, hidden Markov models (HMMs) are employed for performing the association of each shot with one of the semantic classes that are of interest. The novel contributions of this paper lie in the areas of motion information processing and representation. Regarding the motion information processing, the kurtosis of the optical flow motion estimates is calculated for identifying which motion values originate from true motion rather than measurement noise. Additionally, unlike the majority of the approaches of the relevant literature that are mainly limited to global- or camera-level motion representations, a new representation for providing local-level motion information to HMMs is also presented. It focuses only on the pixels where true motion is observed. For the selected pixels, energy distribution-related information, as well as a complementary set of features that highlight particular spatial attributes of the motion signal, are extracted. Experimental results, as well as comparative evaluation, from the application of the proposed approach in the domains ofTennis,NewsandVolleyballbroadcast video, andHuman Actionvideo demonstrate the efficiency of the proposed method.

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

In this paper, a novel transform-based, blind and robust 3-D mesh watermarking scheme is presented. The 3-D surface of the mesh is firstly divided into a number of discrete continuous regions, each of which is successively sampled and mapped onto oblate spheroids, using a novel surface parameterization scheme. The embedding is performed in the spheroidal harmonic coefficients of the spheroids, using a novel embedding scheme. Changes made to the transform domain are then reversed back to the spatial domain, thus forming the watermarked 3-D mesh. The embedding scheme presented herein resembles, in principal, the ones using the multiplicative embedding rule (inherently providing high imperceptibility). The watermark detection is blind and by far more powerful than the various correlators typically incorporated by multiplicative schemes. Experimental results have shown that the proposed blind watermarking scheme is competitively robust against similarity transformations, connectivity attacks, mesh simplification and refinement, unbalanced resampling, smoothing and noise addition, even when juxtaposed to the informed ones.

In this paper, a novel approach for 3-D Shape description and retrieval based on the theory of ellipsoidal harmonics is presented. Four novel descriptors are introduced: the surface ellipsoidal harmonics descriptor, which concerns 3-D objects that are described as polygonal surfaces; the volumetric ellipsoidal harmonics descriptor, which is applicable to volumetric 3-D objects; the generalized ellipsoidal harmonics descriptor that is applied to any local 3-D object descriptors; and, finally, the combined ellipsoidal-spherical harmonics descriptor, which leads to a compact and powerful descriptor that inherits the advantages of both approaches: the rotation invariance properties of the spherical harmonics and the directional information enclosed in ellipsoidal harmonics. Experimental results performed using well-known 3-D object databases prove the retrieval efficiency of the proposed approach.

In this paper, we explore bilinear and elastically deformable models for addressing jointly 3D face and facial expression recognition. An elastically deformable model is built first to allow anatomically valid point-to-point correspondence among face surfaces and then, bilinear models are used to decouple the impact of identity and expression on face appearance. This enables the representation of the surface using two independent sets of control coefficients that can be used for joint face and facial expression recognition. The proposed system was tested on the publicly available BU-3DFE face database where an average facial expression recognition rate of 89.5% and a rank-1 face recognition rate of 85% were achieved.

In this paper, we present a novel approach for 3D facial expression recognition which is inspired by the advances of ant colony and particle swarm optimization (ACO and PSO respectively) in the field of data mining. Anatomical correspondence between faces is first established using a generic 3D face model which is deformed elastically to match the facial surfaces. Surface points are then used as a basis for classification according to a set of classification rules, which are discovered by an ACO/PSO-based rule discovery algorithm. The performance of the proposed algorithm has been evaluated on the BU-3DFEDB facial expression database where a total recognition rate of 92.3% was achieved.

A novel gait authentication scheme based on distributed source coding principles is proposed. Biometric recognition is formulated as a coding problem with noisy side information at the decoder and error correcting codes are employed for user authentication. The effective exploitation of the noise channel statistics in the decoding process improves performance. It is also shown that the proposed method increases the security of the stored biometric templates. Gait recognition is based on the extraction of depth data from human silhouettes and a set of discriminative features. The experimental results demonstrate the validity of the proposed method.

In this paper, a motion-based approach for detecting high-level semantic events in video sequences is presented. Its main characteristic is its generic nature, i.e. it can be directly applied to any possible domain of concern without the need for domain-specific algorithmic modifications or adaptations. For realizing event detection, the examined video sequence is initially segmented into shots and for every resulting shot appropriate motion features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing the association of each shot with one of the high-level semantic events that are of interest in any given domain. Regarding the motion feature extraction procedure, a new representation for providing local-level motion information to HMMs is presented, while motion characteristics from previous frames are also exploited. Experimental results as well as comparative evaluation from the application of the proposed approach in the domain of news broadcast video are presented.

In this paper, we explore bilinear models for jointly addressing 3D face and facial expression recognition. An elastically deformable model algorithm that establishes correspondence among a set of faces is proposed first and then bilinear models that decouple the identity and facial expression factors are constructed. Fitting these models to unknown faces enables us to perform face recognition invariant to facial expressions and facial expression recognition with unknown identity. A quantitative evaluation of the proposed technique is conducted on the publicly available BU-3DFE face database in comparison with our previous work on face recognition and other state-of-the-art algorithms for facial expression recognition. Experimental results demonstrate an overall 90.5% facial expression recognition rate and an 86% rank-1 face recognition rate.

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval.

A novel surveillance system integrating 2D and 3D facial data is presented in this paper, based on a low-cost sensor capable of real-time acquisition of 3D images and associated color images of a scene. Depth data is used for robust face detection, localization and 3D pose estimation, as well as for compensating pose and illumination variations of facial images prior to classification . The proposed system was tested under an open-set identification scenario for surveillance of humans passing through a relatively constrained area. Experimental results demonstrate the accuracy and robustness of the system under a variety of conditions usually encountered in surveillance applications.

In this paper, a method suitable for partial matching between 3D objects is presented. The 3D objects are firstly segmented into meaningful parts extending a method which is based on the medial surface of the objects. Then, geometric features are extracted for each part using the spherical trace transform. The extracted features are combined and their covariance matrix is computed as a descriptor of each part. The contribution of the proposed approach is that a meaningful segmentation of 3D objects based on medial surface is achieved and that partial matching is performed on meaningful parts, in a rotation, translation and scaling invariant manner. The experimental results performed, proved that the proposed approach achieves accurate partial matching results in terms of distinct meaningful parts as well as satisfactory overall accuracy.

Computational approaches for protein classification have been proposed over the last years in order to speed up the analysis of the biological mechanics in living organisms. Most of the approaches tend to focus in geometrical comparison of the 3D molecules to reach their goals. In this paper a method suitable for partial (sub)graph matching of 3D proteinic models, in order to achieve fast and accurate classification, is proposed. The 3D objects are firstly segmented to their molecular structure. Then, descriptors are extracted for each segment using spherical harmonics algorithms, and graphs are constructed for the molecules. Next, a sub-graph matching procedure is utilized and the results are refined using biochemical properties to get biological meaningful classification. The experimental results proved that the proposed method achieves accurate classification of the proteinic data.

This paper addresses the problem of frame interpolation for Wyner-Ziv video coding. A novel frame interpolation method based on block-adaptive matching algorithm for motion estimation is presented. This scheme enables block size adaptation to local activity within frames using block merging and splitting techniques. The efficiency of the proposed method is evaluated in transform domain Wyner-Ziv video coding. The experimental results demonstrate the superiority of the proposed method over existing frame interpolation techniques.

In this paper, a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3D object. The descriptor is based on taking "snapshots" of the surface over each point using a virtual camera oriented perpendicularly to the surface. This representation has the advantage of imposing minimal loss of information be robust to self-occlusions and also be very efficient to compute. Then, we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap.

The performance of automatic 3-D face recognition can be significantly improved by coping with the nonrigidity of the facial surface. In this paper, we propose a geodesic polar parameterization of the face surface. With this parameterization, the intrinsic surface attributes do not change under isometric deformations and, therefore, the proposed representation is appropriate for expression-invariant 3-D face recognition. We also consider the special case of an open mouth that violates the isometry assumption and propose a modified geodesic polar parameterization that also leads to invariant representation. Based on this parameterization, 3-D face recognition is reduced to the classification of expression-compensated 2-D images that can be classified with state-of-the-art algorithms. Experimental results verify theoretical assumptions and demonstrate the benefits of the geodesic polar parameterization on 3-D face recognition.

This paper presents a novel layered and fast framework for real-time collision detection and haptic interaction in virtual environments based on superquadric virtual object modeling. An efficient algorithm is initially proposed for decomposing the complex objects into subobjects suitable for superquadric modeling, based on visual salience and curvature constraints. The distance between the superquadrics and the mesh is then projected onto the superquadric surface, thus generating a distance map (SQ-Map). Approximate collision detection is then performed by computing the analytical equations and distance maps instead of triangle per triangle intersection tests. Collision response is then calculated directly from the superquadric models and realistic smooth force feedback is obtained using analytical formulae and local smoothing on the distance map. Experimental evaluation demonstrates that SQ-Map reduces significantly the computational cost when compared to accurate collision detection methods and does not require the huge amounts of memory demanded by distance field-based methods. Finally, force feedback is calculated directly from the distance map and the superquadric formulae.

We propose a novel scheme for error resilient image transmission. The proposed scheme employs a product coder consisting of LDPC codes and RS codes in order to deal effectively with bit errors. The efficiency of the proposed scheme is based on the exploitation of determinate symbols in Tanner graph decoding of LDPC codes and a novel product code optimization technique based on error estimation. Experimental evaluation demonstrates the superiority of the proposed system in comparison to recent state-of-the art techniques for image transmission.

We present a novel scheme for the transmission of H.264/AVC video streams over lossy packet networks. The proposed scheme exploits the error resilient features of H.264/AVC codec and employs Reed-Solomon codes to protect effectively the streams. The optimal classification of macroblocks into slice groups and the optimal channel rate allocation are achieved by iterating two interdependent steps. Simulations clearly demonstrate the superiority of the proposed method over other recent algorithms for transmission of H.264/AVC streams

In this paper, a 3D shape-based approach is presented for the efficient search, retrieval, and classification of protein molecules. The method relies primarily on the geometric 3D structure of the proteins, which is produced from the corresponding PDB files and secondarily on their primary and secondary structure. After proper positioning of the 3D structures, in terms of translation and scaling, the Spherical Trace Transform is applied to them so as to produce geometry-based descriptor vectors, which are completely rotation invariant and perfectly describe their 3D shape. Additionally, characteristic attributes of the primary and secondary structure of the protein molecules are extracted, forming attribute-based descriptor vectors. The descriptor vectors are weighted and an integrated descriptor vector is produced. Three classification methods are tested. A part of the FSSP/DALI database, which provides a structural classification of the proteins, is used as the ground truth in order to evaluate the classification accuracy of the proposed method. The experimental results show that the proposed method achieves more than 99 percent classification accuracy while remaining much simpler and faster than the DALI method.

In this paper, a biometric authentication system based on measurements of the user's three-dimensional (3-D) hand geometry is proposed. The system relies on a novel real-time and low-cost 3-D sensor that generates a dense range image of the scene. By exploiting 3-D information we are able to limit the constraints usually posed on the environment and the placement of the hand, and this greatly contributes to the unobtrusiveness of the system. Efficient, close to real-time algorithms for hand segmentation, localization and 3-D feature measurement are described and tested on an image database simulating a variety of working conditions. The performance of the system is shown to be similar to state-of-the-art hand geometry authentication techniques but without sacrificing the convenience of the user.

The transmission of JPEG2000 images over wireless channels is examined using reorganization of the compressed images into error-resilient, product-coded streams. The product-code consists of Turbo-codes and Reed-Solomon codes which are optimized using an iterative process. The generation of the stream to be transmitted is performed directly using compressed JPEG2000 streams. The resulting scheme is tested for the transmission of compressed JPEG2000 images over wireless channels and is shown to outperform other algorithms which were recently proposed for the wireless transmission of images.

We propose a novel scheme for error-resilient image transmission. The proposed scheme employs a product coder consisting of low-density parity check (LDPC) codes and Reed-Solomon codes in order to deal effectively with bit errors. The efficiency of the proposed scheme is based on the exploitation of determinate symbols in Tanner graph decoding of LDPC codes and a novel product code optimization technique based on error estimation. Experimental evaluation demonstrates the superiority of the proposed system in comparison to recent state-of-the-art techniques for image transmission.

Measuring the similarity between three-dimensional (3-D) objects is a challenging problem, with applications in computer vision, molecular biology, computer graphics, and many other areas. This paper describes a novel method for 3-D model content-based search based on the 3-D Generalized Radon Transform and a querying by-3-D-model approach. A set of descriptor vectors is extracted using the Radial Integration Transform (RIT) and the Spherical Integration Transform (SIT), which represent significant shape characteristics. After the proper alignment of the models, descriptor vectors are produced which are invariant in terms of translation, scaling and rotation. Experiments were performed using three different databases and comparing the proposed method with those most commonly cited in the literature. Experimental results show that the proposed method is adequately satisfactory in terms of both precision versus recall and time needed for retrieval, and that it can be used for 3-D model search and retrieval in a highly efficient manner.

In this paper, the 2D polar wavelet transform is proposed for content based search and retrieval of 3D objects. After the decomposition of a 3D object's volume into a set of planes, the 2D polar wavelet transform is applied to each of them, generating a set of 2D rotation invariant features. These features comprise the input to the spherical trace transform for the final descriptor extraction, which is used for the shape matching. The 2D wavelet transform demonstrated experimentally a high discriminative power, as compared to other existing methods.

In this paper a 3D shape-based approach is presented for the efficient search, retrieval and classification of protein molecules. The method relies on the geometric 3D appearance of the proteins, which is produced from the corresponding PDB files. After proper positioning and alignment of the 3D structures, in terms of translation and scaling, the 3D structures are decomposed into planes. Then, the polar Fourier transform is applied to the planes creating a new domain of concentric spheres. In this new domain a set of functionals is applied so as to produce descriptor vectors, which are completely invariant to rotation and perfectly describe their 3D shape. Experimental results performed using a portion of the FSSP/DALI database shoed that the proposed method achieves more than 98% classification accuracy with less complexity and much simplicity and it is very fast comparing with the DALI method.

Systems that provide ground movement management at airports, maintaining ground safety and increasing air traffic capacity, are called A-SMGCS (advanced surface movement guidance and control systems). However, common A-SMGCS systems, based on surface movement radars (SMR), are affected by limitations in their coverage due to reflections or shadows from objects on the airport surface. Hence, the use of a complementary system is necessary to act as a "gap-filler", i.e. to provide accurate ground movement information for these problematic areas. This paper aims to present a novel cost-effective video-based system to act as a "gap-filler" for existing A-SMGCS systems. The proposed system consists of a network of intelligent digital cameras, which are able to detect the presence of an aircraft or vehicle in specific locations within their fields of view, and then provide this information to a multiple hypothesis tracking (MHT) algorithm to extract target tracks.

Efficient video content management and exploitation requires extraction of the underlying semantics, a non-trivial task associating low-level features of the image domain and high-level semantic descriptions. In this paper, a knowledge-assisted approach for extracting semantics of domain-specific video content is presented. Domain knowledge considers both low-level features (color, motion, shape) and spatial behavior (topological and directional information). During the preprocessing step, a set of over-segmented homogenous atom-regions is generated and their low-level and spatial descriptions are extracted. A genetic algorithm is then applied in order to find the optimal interpretation according to a specific domain conceptualization. The proposed approach was tested on the formula one, tennis and beach vacations domains showing promising results.

The present paper describes an enhanced haptic virtual reality application for geometry education. The proposed application allows the user to create and edit a scene that consists of three-dimensional geometrical objects in order to form and solve complex geometrical problems. The core of the proposed scheme is based on a novel interference detection algorithm, which utilizes implicit surfaces, such as superquadrics, and their analytical description to speed up collision detection between the virtual hand and the virtual environment. Intersection tests are executed utilizing the implicit analytical formulae of the superquadrics. Experimental results demonstrate the high applicability of the proposed application and the huge gain in speed of the proposed collision detection approach when compared to state of the art methods.

An approach to knowledge-assisted semantic video object detection based on a multimedia ontology infrastructure is presented. Semantic concepts in the context of the examined domain are defined in an ontology, enriched with qualitative attributes (e.g., color homogeneity), low-level features (e.g., color model components distribution), object spatial relations, and multimedia processing methods (e.g., color clustering). Semantic Web technologies are used for knowledge representation in the RDF(S) metadata standard. Rules in F-logic are defined to describe how tools for multimedia analysis should be applied, depending on concept attributes and low-level features, for the detection of video objects corresponding to the semantic concepts defined in the ontology. This supports flexible and managed execution of various application and domain independent multimedia analysis tasks. Furthermore, this semantic analysis approach can be used in semantic annotation and transcoding systems, which take into consideration the users environment including preferences, devices used, available network bandwidth and content identity. The proposed approach was tested for the detection of semantic objects on video data of three different domains.

This paper presents a novel object-based method for the generation of a stereoscopic image sequence from a monoscopic video, using bidirectional two-dimensional motion estimation for the recovery of rigid motion and structure and a Bayesian framework to handle occlusions. The latter is based on extended Kalman filters and an efficient method for reliably tracking object masks. Experimental results show that the layered object scene representation, combined with the proposed algorithm for reliably tracking object masks throughout the sequence, yields very accurate results.

A novel image transmission scheme is proposed for the communication of set partitioning in hierarchical trees image streams over wireless channels. The proposed scheme employs turbo codes and Reed-Solomon codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the compressed bitstream is also proposed and applied in conjunction with an inherently more efficient technique for product code decoding. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed transmission system in comparison to well-known robust coding schemes.

This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of background clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

Ultrasound imaging allows the evaluation of the degree of emergency of a patient. However, in some instances, a well-trained sonographer is unavailable to perform such echography. To cope with this issue, the Mobile Tele-Echography Using an Ultralight Robot (OTELO) project aims to develop a fully integrated end-to-end mobile tele-echography system using an ultralight remote-controlled robot for population groups that are not served locally by medical experts. This paper focuses on the user interface of the OTELO system, consisting of the following parts: an ultrasound video transmission system providing real-time images of the scanned area, an audio/video conference to communicate with the paramedical assistant and with the patient, and a virtual-reality environment, providing visual and haptic feedback to the expert, while capturing the expert's hand movements. These movements are reproduced by the robot at the patient site while holding the ultrasound probe against the patient skin. In addition, the user interface includes an image processing facility for enhancing the received images and the possibility to include them into a database.

The present paper proposes a novel method for content-based search in a database of VRML three-dimensional (3-D) models. The proposed technique is based on a querying-by-3-D-model approach. A set of shape-based descriptors are extracted from the reference 3-D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3-D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3-D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3-D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method.

We propose an innovative environment for presenting products through the Internet using modern visualization techniques and providing high level of interaction with the user. The environment consists of three main modules, namely a) the scene-authoring tool, b) the interactive scene-viewer and c) the assembly-demonstration tool. These modules are bound together with the utilization of a robust ontology-based knowledge management system.

This paper describes a novel method for 3D model content-based search and retrieval based on the 3D radon transform and a querying-by-3D-model approach. Descriptors are extracted using the 3D radon transform and applying a number of functionals on the transform's coefficients. Similarity measures are then created for the extracted descriptors and introduced into a 3D model-matching algorithm. This results in a very fast and accurate matching method. Experimental results are presented evaluating the performance of the proposed method in terms of precision versus recall diagrams.

In this paper, a novel framework for multiple description coding of video is presented. The proposed scheme is based on a wavelet source coder and an efficient methodology for the generation of multiple descriptions. An algorithm is also presented for the optimal, in the sense of maximizing reconstruction quality, allocation of redundancy among the descriptions. Experimental results for the transmission of video using two descriptions demonstrate the efficiency of the proposed method.

A novel method for the watermarking of 3D models is proposed, which is robust to geometric distortions such its rotation, translation and uniform scaling. After proper positioning and alignment of the 3D models, a watermark is embedded in the vertices of the model using a robust technique for modifying imperceptibly the location of a subset of the 3D model vertices. The watermark can be used as a link to an identifier for the 3D model and the entire system can be used for data hiding applications. One application is the use of watermark as a link to 3D model descriptors for content-based search and retrieval. Experimental results show the ability of the proposed method to the aforementioned attacks. The proposed method is also robust against vertex reordering attack.

The paper describes a face recognition system using a combination of color and depth images. To cope with illumination and pose variations 3D information is used for the normalization of the input images. The proposed pose compensation algorithm is based on a robust 3D face detection and pose estimation technique, while illumination compensation exploits depth data to recover the illumination of the scene and relight the image under frontal lighting. When normalized images, depicting upright orientation and frontal lighting, are used for classification significantly high recognition rates are achieved, as demonstrated on a face database with more than 2000 images.

A novel approach to domain-specific video analysis is proposed. The proposed approach is based on exploiting domain-specific knowledge in the form of an ontology to detect video objects corresponding to the semantic concepts defined in the ontology. The association between the visual objects and the defined semantic concepts is performed by taking into account both qualitative attributes of the semantic objects (e.g., color homogeneity), indicating necessary preprocessing methods (color clustering, respectively), and numerical data generated via training (e.g., color models, also defined in the ontology). To enable fast and efficient processing, this methodology is applied to MPEG-2 video, requiring only its partial decoding. The proposed approach is demonstrated in the domain of Formula-1 racing video and shows promising results.

This paper presents a novel pyramidal representation scheme for deformable object modelling, which uses a hierarchical approach to optimize the system's performance by executing the simulation in every level of a pyramid. The simulation results of each level are used to predict the lower level's state. The prediction is used as initial guess for the simulation of the lower level. The above procedure is repeated until the final level is reached. Experimental evaluation demonstrates that the proposed scheme is able to reduce significantly the computational cost, especially when the simulation involves procedures, which need large numerical computation like the conjugate gradient in implicit integration schemes.

A novel scheme is proposed for the transmission of JPEG2000 image streams over wireless channels. The proposed scheme exploits the block-based coding structure of the JPEG2000 streams and employs optimized product codes consisting of Turbo codes and Reed-Solomon codes in order to deal effectively with burst errors. The optimization is based on information extracted directly from the compressed JPEG2000 streams. Experimental evaluation demonstrates that the proposed scheme outperform other recent algorithms for the wireless transmission of images.

The SCHEMA Network of Excellence aims to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. In this paper, advances in the development of the SCHEMA reference system are reported, focusing on the application of region-based image retrieval using automatic segmentation. More specifically, the integration of four segmentation algorithms and the MPEG-7 eXperimentation Model with the reference system are discussed, along with the motivation behind these and various other choices that were made during the development of the reference system. Experimental results for this system, as well as results for an earlier version of it employing proprietary descriptors, are shown using a common collection of images. Comparative evaluation of these versions, both in terms of retrieval accuracy and in terms of time-efficiency, allows the evaluation of the reference system as a whole as well as the evaluation of the usability of different components integrated with it, such as the MPEG-7 eXperimentation Model. These results illustrate the efficiency of the proposed system, as well as its suitability in serving as a test-bed for evaluating and comparing different algorithms and approaches pertaining to the content-based and semantic manipulation of visual information.

This paper presents a novel approach in 3D content-based search and retrieval. First, a set of functional are applied on a 3D model's volume producing a new domain of concentric spheres. In this new domain a new set of functionals is applied, resulting to a completely rotation invariant descriptor vector, which is used for 3D model matching. Experiments were performed using a database and comparing the proposed method with the MPEG-7 3D shape spectrum descriptor. Experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.

In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised operation of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image segmentation scheme employs the aforementioned segmentation algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

The widespread use of digital multimedia data has increased the need for effective means of copyright protection. Watermarking has attracted much attention, as it allows the embedding of a signature in a digital document in an imperceptible manner. In practice, watermarking is subject to various attacks, intentional or unintentional, which degrade the embedded information, rendering it more difficult to detect. A very common, but not malicious, attack is quantization, which is unavoidable for the compression and transmission of digital data. The effect of quantization attacks on the nearly optimal Cauchy watermark detector is examined in this paper. The quantization effects on this detection scheme are examined theoretically, by treating the watermark as a dither signal. The theory of dithered quantizers is used in order to correctly analyze the effect of quantization attacks on the Cauchy watermark detector. The theoretical results are verified by experiments that demonstrate the quantization effects on the detection and error probabilities of the Cauchy detection scheme.

In this paper, a novel algorithm is presented for the real-time, compressed-domain, unsupervised segmentation of image sequences and is applied to video indexing and retrieval. The segmentation algorithm uses motion and color information directly extracted from the MPEG-2 compressed stream. An iterative rejection scheme based on the bilinear motion model is used to effect foreground/background segmentation. Following that, meaningful foreground spatiotemporal objects are formed by initially examining the temporal consistency of the output of iterative rejection, clustering the resulting foreground macroblocks to connected regions and finally performing region tracking. Background segmentation to spatiotemporal objects is additionally performed. MPEG-7 compliant low-level descriptors describing the color, shape, position, and motion of the resulting spatiotemporal objects are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. This, combined with a relevance feedback mechanism, allows the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and the retrieval of relevant video segments. Desired spatial and temporal relationships between the objects in multiple-keyword queries can also be expressed, using the shot ontology. Experimental results of the application of the segmentation algorithm to known sequences demonstrate the efficiency of the proposed segmentation approach. Sample queries reveal the potential of employing this segmentation algorithm as part of an object-based video indexing and retrieval scheme.

A novel unsupervised video object segmentation algorithm is presented, aiming to segment a video sequence to objects: spatiotemporal regions representing a meaningful part of the sequence. The proposed algorithm consists of three stages: initial segmentation of the first frame using color, motion, and position information, based on a variant of the K-means-with-connectivity-constraint algorithm; a temporal tracking algorithm, using a Bayes classifier and rule-based processing to reassign changed pixels to existing regions and to efficiently handle the introduction of new regions; and a trajectory-based region merging procedure that employs the long-term trajectory of regions, rather than the motion at the frame level, so as to group them to objects with different motion. As shown by experimental evaluation, this scheme can efficiently segment video sequences with fast moving or newly appearing objects. A comparison with other methods shows segmentation results corresponding more accurately to the real objects appearing on the image sequence.

A novel image sequence segmentation method which combines both spatial and temporal information is presented in this paper. The first step is an intensity segmentation scheme based on the edgeflow method. The temporal information is introduced through three-dimensional (3-D) motion estimation parameters. In the second step, regions obtained from the first step are clustered according to their 3-D motion models. In order to reduce the noise sensitivity of the motion estimation process, we introduce a robust method which produces accurate motion parameters and facilitates the correct clustering that follows. This ensures that rigid objects with luminance discontinuities can be segmented correctly. The method has been successfully tested in real imagery and typical examples are presented in this paper.

The issue of copyright protection of digital multimedia data has attracted a lot of attention during the last decade. An efficient copyright protection method that has been gaining popularity is watermarking, i.e., the embedding of a signature in a digital document that can be detected only by its rightful owner. Watermarks are usually blindly detected using correlating structures, which would be optimal in the case of Gaussian data. However, in the case of DCT-domain image watermarking, the data is more heavy-tailed and the correlator is clearly suboptimal. Nonlinear receivers have been shown to be particularly well suited for the detection of weak signals in heavy-tailed noise, as they are locally optimal. This motivates the use of the Gaussian-tailed zero-memory nonlinearity, as well as the locally optimal Cauchy nonlinearity for the detection of watermarks in DCT transformed images. We analyze the performance of these schemes theoretically and compare it to that of the traditionally used Gaussian correlator, but also to the recently proposed generalized Gaussian detector, which outperforms the correlator. The theoretical analysis and the actual performance of these systems is assessed through experiments, which verify the theoretical analysis and also justify the use of nonlinear structures for watermark detection. The performance of the correlator and the nonlinear detectors in the presence of quantization is also analyzed, using results from dither theory, and also verified experimentally.

In this paper a head tracking algorithm using 3D data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. By not relying on brightness information, the proposed system guarantees robustness under various illumination conditions, and content of the scene. The main novelty of the proposed algorithms, with respect to other head tracking techniques, is the capability for accurate tracking of the 6 degrees of freedom of the head by explicitly utilising 3D head-shoulder geometry. A Bayesian tracking framework is also proposed for continuous 3D head pose estimation. The proposed system has been tested in a real-time application scenario.

In this paper, an image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions. Low-level features describing the color, position, size and shape of the resulting regions are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) in a human-centered fashion. When querying, clearly irrelevant image regions are rejected using the intermediate-level descriptors; following that, a relevance feedback mechanism employing the low-level features is invoked to produce the final query results. The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captions or require manual evaluation and annotation of every image of the collection, and query-by-example approaches, which assume that the user queries for images similar to one that already is at his disposal.

This paper addresses the reconstruction of 3D human body models from 2D video sequences. Considering that the input frames are already segmented, the proposed technique consists of three stages. These stages are independently applied over each segmented frame. Firstly, a skeleton of a human figure obtained from the segmented image is extracted by means of a fast algorithm based on a Voronoi diagram of the boundary points. Afterwards, the skeleton is labelled according to the human body parts (e.g. head, upper arm, lower arm, torso, etc). Secondly, an initial 3D model posture is estimated from the labelled skeleton. Finally, an iterative closest point (ICP) implementation is used to refine the initial model posture by maximizing the similarity between the projected 3D model and the segmented image. Experimental results with video sequences are presented.

A novel image transmission scheme is proposed for the communication of SPIHT image streams over wireless channels. The proposed scheme employs turbo codes and erasure-correction codes in order to deal effectively with burst errors. An algorithm for the optimal unequal error protection of the compressed bitstream is also proposed. The resulting scheme is tested for the transmission of images over wireless channels. Experimental evaluation clearly demonstrates the superiority of the proposed scheme in comparison to well-known robust coding schemes.

A novel embedded wavelet coding scheme is proposed for the transmission of images over unreliable channels. The proposed scheme is based on the partitioning of information into a number of layers which can be decoded independently provided that some important and highly protected information is initially errorlessly transmitted to the decoder. Forward error correction is used in conjunction with the error-resilient source coder for the protection of the compressed stream. Unlike many other robust coding schemes presented to date, the proposed scheme is able to decode portions of the bitstream even after the occurrence of uncorrectable errors. This coding strategy is very suitable for application with block coding schemes such as defined by the JPEG2000 standard. The proposed scheme is compared with other robust image coders and is shown to be very suitable for transmission of images over memoryless channels.

The paper presents a novel watermarking scheme able to resist geometric attacks. The proposed method performs imperceptible watermarking of images in the spatial domain. To generate resistance to scaling and rotation attacks, two generalized Radon transformations of the image are introduced, while resistance to translation is accomplished through a localization of the watermarking method based on feature points of the image. The original image is not required for the detection process. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks including common geometric attacks.

A novel approach to large-format image segmentation is presented, focused on usage in content-based multimedia applications. The proposed framework aims at facilitating the time-efficient segmentation of large-format images while maintaining the high perceptual quality of the segmentation result. For this to be achieved, the employed segmentation algorithm is applied to reduced versions of the large-format images, in order to speed-up its execution, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced by an enhancement stage that involves partial reclassification of the pixels of the original image using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

An error-resilient coding scheme is proposed for the transmission of images over unreliable channels. Forward error correction is used in conjunction with the error-resilient source coder for the protection of the compressed stream. Unlike almost all other robust coding schemes presented to date, the proposed scheme is able to decode portions of the bitstream even after the occurrence of uncorrectable errors. The resulting coder is shown to be very efficient for image transmission over noisy channels.

The decoding of convolutional codes using the list Viterbi algorithm is proposed for data hiding applications. The performance of this technique is evaluated for wavelet-domain information hiding and is shown in many cases to be advantageous in comparison to the widely used turbo codes for efficient extraction of information embedded in digital images.

In this paper an object-based method to generate additional stereo views from monoscopic image sequences using rigid motion and structure estimation by extended Kalman filters is presented. First, rigid scene objects are segmented and feature points in each object are extracted and tracked throughout the video sequence. Then, motion, structure and focal length are estimated recursively for each object, using extended Kalman filters, as described in Azarbayejani and Pentland (1995). Furthermore, the feature points and depths in the stereo image are computed and interpolated using 2-D Delaunay triangulation. Finally, a stereo image generation algorithm is proposed that uses the camera and structure equations to project the 3-D points in a new virtual stereo view for each frame. The generation of stereoscopic scenes is possible even when multiple moving rigid objects exist in the scene. Experimental results show that a layered stereo object representation yields improved results.

A new technique for watermarking of MPEG compressed video streams is proposed. The watermarking scheme operates directly in the domain of MPEG program streams. Perceptual models are used during the embedding process in order to preserve the video quality. The watermark is embedded in the compressed domain and is detected without the use of the original video sequence. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks. The resulting watermarking system is fast and reliable, and is suitable for copyright protection and real-time content authentication applications.

A novel hybrid frequency and spatial domain method is presented for blockiness reduction in low bit rate compressed images. The proposed method consists of two stages acting complementarily. In the first, better estimates of the reconstructed DCT coefficients are obtained based on their observed probability distribution. In the second, an efficient postprocessing scheme, consisting of a region classification algorithm and spatial adaptive filtering, is applied. Experimental results illustrating the performance of the proposed method are presented and evaluated.

We propose novel algorithms for stereoscopic image coding based on the hierarchical decomposition of stereo information. The proposed schemes, based on the wavelet transform and zerotree quantization, are endowed with excellent progressive transmission capability and retain the option for perfect reconstruction of the original image pair. Experimental evaluation shows that the resulting methods produce superior results when compared with other algorithms for stereo image coding. This is achieved without introducing blocking artifacts and with the valuable additional convenience of the production of embedded bitstreams.

An integral part of the MPEG-4 standard is the definition of face animation parameters (FAPs). This paper presents a method for the determination of FAPs by using three dimensional (3-D) rigid and nonrigid motion of human facial features found from two-dimensional (2-D) image sequences. The proposed method assumes that a 3-D model has been fitted to the first frame of the sequence, tracks the motion of characteristic facial features, calculates the 3-D rigid and nonrigid motion of facial features, and through this, estimates the FAPs as defined by the MPEG-4 coding standard. The 2-D tracking process is based on a novel enhanced version of the algorithm proposed by Kanade, Lucas, and Tomasi (1994, 1991). The nonrigid motion estimation is achieved using the same tracking mechanism guided by the facial motion model implied by the MPEG-4 FAPs.

A novel frequency-domain technique for image blocking artifact detection and reduction is presented. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.

A method is presented for the optimization of arbitrary quantizers by use of a compensating postfilter. It is shown that the resulting optimized quantizers fit the model of a linear time-invariant filter followed by additive noise uncorrelated with the input which also characterizes the optimal (Lloyd-Max) quantizers. On the basis of this model, an expression for the variance of the error of a subband coder using optimized quantizers is explicitly determined. Given analysis filters which statistically separate the subbands, it is shown that this variance is minimized if these synthesis filters are chosen, which would achieve perfect reconstruction in lossless coding. The globally optimum filter bank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. A novel method for the determination of optimal bit allocation to subbands of the filter banks with optimized quantizers is also developed. The results are evaluated experimentally by comparison of the optimum uniformly split subband image coding scheme to classical logarithmically-split filter bank (wavelet) coding methods.

A procedure is described for the spatiotemporal segmentation and tracking of objects in colour image sequences. For this purpose, we propose the novel procedure of the k-means with a connectivity constraint algorithm as a general segmentation algorithm combining several types of information including colour, motion and compactness. A new colour distance is also defined for this algorithm. The regularisation parameters are evaluated automatically using the min-max criterion. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames is analyzed simultaneously and as a result the same region is present in consequent frames. Experimental results on real and synthetic colour data demonstrate the performance of the data.

A novel frequency domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the DCT coefficients are modeled by a Laplacian probability function. Experimental results illustrating the performance of the proposed method are presented and evaluated.

A general method to estimate MPEG-4 body animation parameters (BAPs) from depth images by using an analysis-by-synthesis technique is presented. A generic body model is first adapted to the specific person geometry. Then, the mean square error between the synthetic depth image, produced by model rendering, and the original depth image is minimized using the downhill simplex minimization method. Use of this error norm is seen to yield improved results, when compared to two alternative error norms, which were also evaluated. Results are presented for the specific application, where six animation parameters of the arm and the palm are estimated. In this application, an initial estimate is obtained by applying an expectation-maximization (EM) algorithm, which identifies three arm parts and two joint positions (elbow, wrist). A significant advantage of this procedure is that the resulting information can be exploited for reducing the search space and for automatic scale adaptation of each body part.

Thepaper proposes a novel method for content-based search in a database of VRML 3D models. The proposed technique is based on a querying-by-3D-model approach. A set of shape-based descriptors are extracted from the reference 3D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method.

A region-based color image indexing and retrieval algorithm is presented. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of the regions. A new color distance is also defined for this algorithm. Based on the extracted regions, characteristic features are estimated using color, texture and shape information. An important and unique aspect of the algorithm is that, in the context of similarity-based querying, the user is allowed to view the internal representation of the submitted image and the query results. Experimental results demonstrate the performance of the algorithm. The development of an intelligent image content-based search engine for the World-Wide Web is also presented, as a direct application of the presented algorithm.

The paper presents several novel 3D image analysis algorithms, applied towards the segmentation and modeling of faces and hands. These are subsequently used to build a face-based authentication system and a system for human-computer interaction based on static and dynamic gestures. The system relies on an active stereo sensor that uses a structured light approach to obtain 3D information. In this paper we demonstrate how the use of 3D information may significantly improve the efficiency of traditional face and gesture recognition techniques that use 2D images only.

A novel frequency domain technique for image blocking artifact reduction is presented. For each block, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope (MSDS), for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT and is constrained by the upper and lower bound of the quantized DCT coefficients. Experimental results illustrating the performance of the proposed method are presented and evaluated.

Summary form only given, as follows. The transmission of the large store of information contained in 3D medical data sets through limited capacity channels is a critical procedure in many telemedicine applications. Techniques are presented for the compression of visualizations of 3D image data for efficient storage and transmission. Methods are first presented for the transmission of the 3D surface of the objects using contour following methods. Alternately, the visualization at the receiver may be based on a series of depth maps corresponding to some motion of the object, specified by the medical observer. Depth maps may be transmitted by using depth map motion compensated prediction. Alternately, a wire-mesh model of the depth map may be formed and transmitted by encoding the motion of its nodes. All these methods are used for the transmission of the 3D image with visualization carried out at the receiver. Methods are also developed for efficient transmission of the images visualized at the encoder site. These methods allow remote interactive manipulation (rotation, translation, zoom) of the 3D objects, and may be implemented even if the receiver is a relatively simple and inexpensive workstation or a simple monitor. In all these cases, the coding of binocular views of the 3D scene is examined and recommendations are made for the implementation of coders of stereo views of 3D medical data.

A novel framework for multiple description coding of video is presented. The proposed scheme is based on an efficient methodology for the generation of multiple descriptions which eliminates drift at the decoder. An arbitrary number of descriptions can be obtained from the same original video sequence. Experimental results for the transmission of video using two descriptions demonstrate the efficiency of the proposed methodology.

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.

This paper presents a novel procedure for the representation and coding of three-dimensional (3-D) surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus, when combined with the appropriate encoding of the coefficients, optimize the compression of the mesh information transmitted. At the same time, it produces a hierarchy of meshes based on quincunx sampling where coarse meshes are as similar to their finer versions as possible. This is very much desirable in progressive transmission. Depending on its interpolation error and the available bitrate, each filtered sample is a candidate for becoming a vertex of the mesh. The result is a progressive sequence of meshes consisting of more triangles wherever large variations exist and fewer in uniform regions. Complete correspondence between triangles at each level is identified, resulting in an efficient hierarchical representation of the mesh. The algorithm can be also used for the triangulation of a specific region of interest. Experimental results demonstrate that the proposed scheme provides an improvement in quality (MSE) by a factor of two when compared with other well known adaptive triangulation schemes.

A novel procedure for segmenting a set of scattered 3D data obtained from a head and shoulders multiview sequence is presented. The procedure consists of two steps. In the first step, two ellipses corresponding to the head and the body of the person are identified based on ellipse fitting of the outline of the person in each image. The fitting is based on a fast direct least squares method using the constraint that forces a general conic to be an ellipse. In order to achieve head/body segmentation, a K-means algorithm is used to minimise the fitting error between the points and the two ellipsoids. In the second step, a 3D ellipsoid model corresponding to the head of the person is identified using an extension of the above method. Robustness and outlier removal can be achieved if a 3D ellipsoid model estimation technique is used in conjunction with the Median of Least Squares (MedLS) technique, which minimises the median of the errors corresponding to each 3D point. An interesting application of the proposed method is the combination of the 3D ellipsoid model with a generic face model which is adapted to the face images to provide information only for the high-detail front part of the head while the 3D ellipsoid is used for the back of the head, which is usually not visible.

We propose a novel algorithm for embedded stereoscopic image coding based on the hierarchical decomposition of stereo information. The proposed scheme is endowed with excellent progressive transmission capability and retains the option for perfect reconstruction of the original image pair. Experimental evaluation shows that the resulting method produces comparable results with a previously proposed algorithm for progressive stereo image coding.

A system for building a three dimensional (3-D) human head model from two camera views is proposed. A generic 3-D face model is adapted to a human face of which the frontal and profile views are given. Then, a 3-D ellipsoid approximation technique is used to fit an ellipsoid to the 3-D wireframe model. This ellipsoid is then used to approximate the back of the head, while the the initial 3-D face model provides information for the high-detail front part of the head. The part of the ellipsoid model that corresponds to the back of the head is then identified and an algorithm to merge the front (face) and the back part is proposed. A cylindrical texture map is finally built covering the entire area of the head by exploiting the inherent face symmetry. The final result is a complete, textured model of a specific person's head.

This paper presents a novel procedure for the representation and coding of 3D surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus optimize the compression of the mesh information transmitted. At the same time, it produces a hierarchy of meshes based on quincunx sampling where coarse meshes are as similar to their finer versions as possible. This is very much desirable in progressive transmission. The result is a progressive sequence of meshes consisting of more triangles wherever large variations exist and fewer in uniform regions. Complete correspondence between triangles at each level is identified, resulting to an efficient hierarchical representation of the mesh. Experimental results demonstrate the efficient performance of the algorithm.

We present a new image sequence segmentation method which combines both spatial and temporal information in a multiresolution framework. A region growing technique in a multiresolution scheme outputs an over-segmented partition of the image scene. Pure temporal information is collected for each region using a feature extraction/feature tracking technique. Motion information is further processed in a pyramidal robust motion estimation scheme, in order to calculate more complex motion parameters for each region. Regions obtained from the first step are clustered according to their motion models. This ensures that rigid objects with luminance discontinuities can be segmented correctly. The method has been successfully tested in real imagery and typical examples are presented.

This paper addresses the problem of adapting a generic 3D face model to a human face of which the frontal and profile views are given. Assuming that a set of feature points have been detected on both views the adaptation procedure initializes with a rigid transformation of the model aiming to minimize the distances of the 3D model feature nodes from the calculated 3D coordinates of the 2D feature points. Then, a non-rigid transformation ensures that the feature nodes are displaced optimally close to their exact calculated positions, dragging their neighbors in a way that does not deform the facial model in an unnatural way.

In this paper we investigate the coding of 3D medical images using three-dimensional wavelet decomposition and adaptive arithmetic coding. A highly efficient context arithmetic coding scheme is used in conjunction with classical lifting-based wavelet decompositions. The resulting coder is shown to attain superior performance in comparison to other compression schemes for 3D medical image coding.

In this paper a procedure is described for the segmentation and tracking of objects in color image sequences. For this purpose, we propose the novel procedure of K-Means with a connectivity constraint algorithm as a general segmentation algorithm combining several types of information including color, motion and compactness. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames is analyzed simultaneously and as a result the same region is present in consequent frames. Experimental results in real image sequences evaluate the performance of the algorithm.

This paper introduces an optimal hierarchical adaptive mesh construction algorithm using the face-centered orthorhombic lattice (FCO) sampling which is a natural extension of the quincunx lattice to the 3-dimensional case. A scheme for construction of adaptive meshes is presented. Initially, a highly detailed and densely sampled regular mesh is obtained from geometry scanning or from a nonoptimal polygon mesh. The adaptive triangle mesh is constructed by using fixed position vertices along with an efficient adaptive triangulation technique. The decimation is based on FCO sampling and surface estimation filters. The result is a progressive sequence of meshes consisting of more triangles wherever sharp edges exist and fewer in uniform plane regions. Experimental results demonstrate the usage and performance of the algorithm.

A novel algorithm for the generation of background sprite images from multiview image sequences is presented. A dynamic programming algorithm, first proposed by Grammalidis and Strintzis (see IEEE Trans. Circuits Syst. Video Technol., vol.8, p.328-44, 1998) using a multiview matching cost, as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. By combining motion, disparity, and occlusion information, a sprite image corresponding to the first (main) view at the first time instant is generated. Image pixels from other views that are occluded in the main view are also added to the sprite. Finally, the sprite coding method defined by MPEG-4 is extended for multiview image sequences based on the generated sprite. Experimental results are presented, demonstrating the performance of the proposed technique and comparing it with standard MPEG-4 coding methods applied independently to each view.

A procedure is described for the segmentation, content-based coding, and visualization of videoconference image sequences. First, image sequence analysis is used to estimate the shape and motion parameters of the person facing the camera. A spatiotemporal filter, taking into account the intensity differences between consequent frames, is applied, in order to separate the moving person from the static background. The foreground is segmented in a number of regions in order to identify the face. For this purpose, we propose the novel procedure of K-means with connectivity constraint algorithm as a general segmentation algorithm combining several types of information including intensity, motion and compactness. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames are analyzed simultaneously and as a result, the same region is present in consequent frames. Based on this information, a 3-D ellipsoid is adapted to the person's face using an efficient and robust algorithm. The rigid 3-D motion is estimated next using a least median of squares approach, Finally, a virtual reality modeling language (VRML) file is created containing all the above information; this file may be viewed by using any VRML 2.0 compliant browser.

The estimation of rigid-body 3-D motion parameters using point correspondences from a pair of images under perspective projection is, typically, very sensitive to noise. We present a novel robust method combining two approaches: (1) the SVD analysis of a linear operator resulting from the feature points and the displacement vectors and (2) a modified version of the well-known weighted least-squares method proposed by Huber in the context of robust statistics. We give a detailed rank analysis of the involved linear operator and study the effects of noise. We also propose a robust method guided by the structure of this operator, using weighted least squares and data partitioning. The method has been tested on artificial data and on real image sequences showing a remarkable robustness, even in the presence of up to 50% outliers in the data set.

Efficient techniques are introduced in this paper for the identification of the occlusion and visible background and foreground areas in a noisy stereoscopic image pair. Three different Bayes decision methods are tested for this purpose. The first, and uses three hypotheses for the formulation of the Bayes decision rules, adopting the right image as a reference. After performing a dual-Bayes decision test having each time as a different image of the stereo pair as reference, consistency checking is added to these tests to form the second method. Finally, four compound hypotheses are used in the third method, which is the most accurate but also the more detailed and computationally involved of three. Experimental results illustrating the performance of the techniques are presented and evaluated.

This paper extends a known efficient technique for rigid three-dimensional (3-D) motion estimation so as to make it applicable to motion estimation problems occuring in image sequence coding applications. The known technique estimates 3-D motion using previously evaluated 3-D correspondence. However, in image sequence coding applications, 3-D correspondence is unknown and usually only two-dimensional (2-D) motion vectors are initially available. The novel neural network (NN) introduced in this paper uses initially estimated 2-D motion vectors to estimate 3-D rigid motion, and is therefore suitable for image sequence coding applications. Moreover, it is shown that the NN introduced in this paper performs extremely well even in cases where 3-D correspondence is known with accuracy. Experimental results are presented for the evaluation of the proposed scheme.

This paper presents a modified JPEG coder that is applied to the compression of mixed documents (containing text, natural images, and graphics) for printing purposes. The modified JPEG coder proposed in this paper takes advantage of the distinct perceptually significant regions in these documents to achieve higher perceptual quality than the standard JPEG coder. The region-adaptivity is performed via classified thresholding being totally compliant with the baseline standard. A computationally efficient classification algorithm is presented, and the improved performance of the classified JPEG coder is verified.

This correspondence introduces a novel method for the construction of extended reduced pyramids with rational decimation ratios from stage to stage. It is shown that this construction produces more accurate interpolation, and thus more efficient lossless compression, than conventional reduced pyramids.

A methodology is presented for the optimal construction of multichannel reduced pyramids by selecting the interpolation synthesis postfilters so as to minimize the error variance at each level of the pyramid. The general optimization methodology is applied for the optimization of pyramids for the compression of electrocardiographic signals and RGB colored images.

In this paper, a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2-D active contour (snake) algorithm integrated with a novel iterative initialization technique, which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.

The estimation of rigid body 3-D motion parameters from perspective views is typically very sensitive to noise and also to the presence of outliers in the measurements. In this paper we present a robust 3-D motion estimation approach based on a previously proposed method using SVD analysis of the measurements matrix. On the introduction of noise and outliers the performance of the old method was seen to deteriorate rapidly. Here the problem is attached by splitting the measurement set into smaller subsets and combining the properties of the resulting submatrices with the properties of the desired solution vector in order to obtain our estimate. The method is very robust and it has been successfully tested in both artificial datasets and real images with up to 50% presence of outliers. In addition, the method is fast and more importantly, the estimate quality is independent of the percentage of outliers.

We first calculate the optimal predictors of a lifting scheme in the general n-dimensional case. Then, we apply these optimal predictor filters with corresponding update filters for the lossless compression of still images using simple row-column sampling. The efficiency of the linear predictor is enhanced by nonlinear means, namely by adaptive-length postprocessing. The resulting filter bank in effect adapts to the features of the area of the image being processed and in this way it is shown to achieve superior performance than even the most efficient known lossless compression methods capable of progressive transmission.

An algorithm to generate background sprite images from multiview image sequences is presented. A dynamic-programming algorithm, first proposed in Grammalidis and Strinzis (1998), using a multiview matching cost as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. By combining motion, disparity and occlusion information a sprite image corresponding to the first (main) view at the first time instant is generated. Image pixels from other views that are occluded in the main view are also added to the sprite. The sprite coding method defined by MPEG-4 is extended for multiview image sequences, based on the generated sprite. Experimental results demonstrating the performance of the proposed technique and comparing it with methods using sprite generation from monoscopic sequences are presented.

In this paper a procedure is described for the segmentation of image sequences. For this purpose, we propose the novel procedure of K-Means with connectivity constraint algorithm as a general segmentation algorithm combining several types of information including intensity, motion and compactness. The algorithm is extended so as to separate and track objects appearing in consequent frames of an image sequence. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames is analyzed simultaneously and as a result the same region is present in consequent frames. Experimental results demonstrate the usage and performance of the algorithm.

This paper describes a 3D model-based unsupervised procedure for the segmentation of multiview image sequences using multiple sources of information. The articulation procedure is based on the homogeneity of parameters, such as rigid 3D motion, color and depth, estimated for each sub-object, which consists of a number of interconnected triangles of the 3D model. The rigid 3D motion of each sub-object for subsequent frames is estimated using a Kalman filtering algorithm taking into account the temporal correlation between consecutive frames. Information from all cameras is combined during the formation of the equations for the rigid 3D motion parameters. The parameter estimation for each sub-object and the 3D model segmentation procedures are interleaved and repeated iteratively until a satisfactory object segmentation emerges. The performance of the resulting segmentation method is evaluated experimentally.

Significant progress has recently been made in lossless image compression using discrete wavelet transforms. The overall performance of these schemes may be further improved by properly designing efficient entropy coders. A new technique is introduced for the implementation of context based adaptive arithmetic entropy coding. This technique is based on the prediction of the value of the current transform coefficient, using a weighted least squares method, in order to achieve appropriate context selection for arithmetic coding. Experimental results illustrate and evaluate the performance of the proposed technique.

The optimal linear Karhunen-Loeve transform (KLT) attains the minimum reconstruction error for a fixed number of transform coefficients assuming that these coefficients do not contain noise. In any real coding system, however, the representation of the coefficients using a finite number of bits requires the presence of quantizers. We formulate the optimal linear transform using a data model that incorporates the quantization noise. Our solution does not correspond to an orthogonal transform and in fact, it achieves a smaller mean squared error (MSE) compared to the KLT, in the noisy case. Like the KLT, our solution depends on the statistics of the input signal, but it also depends on the bit-rate used for each coefficient. Especially for images, based on our optimality theory, we propose a simple modification of the discrete cosine transform (DCT). Our coding experiments show a peak signal-to noise ratio (SNR) performance improvement over JPEG of the order of 0.2 dB with an overhead less than 0.01 b/pixel.

Optimal mechanisms are determined for the hierarchical decomposition of wire-frame surfaces generated by box splines. A family of box splines with compact support, suitable for the approximation of wire-frames is first defined, generated by arbitrary sampling matrices with integer eigenvalues. For each such box spline, the optimal positioning of the wire-frame nodes is determined for each level of the hierarchical wire-frame decomposition. Criterion of optimality is the minimization of the variance of the error difference between the original surface and its representation at each resolution level. This is needed so as to ensure that the wire mesh produces at each resolution as close a replica of the original surface as possible. Several such combinations of box spline generated meshes and the corresponding optimal node lattice sequences are examined in detail with a view to practical application. Their specific application to the hierarchical coding of three-dimensional (3-D) wire meshes is experimentally evaluated.

In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented.

In a typical linear data compression system the representation variables resulting from the coding operation are assumed totally reliable and therefore the solution in the mean-squared-error sense is an orthogonal projector to the so-called principal component subspace. When the representation variables are contaminated by additive noise which is uncorrelated with the signal, the problem is called noisy principal component analysis (NPCA) and the optimal MSE solution is not a trivial extension of PCA. We first show that the problem is not well defined unless we impose explicit or implicit constraints on either the coding or the decoding operator. Second, orthogonality is not a property of the optimal solution under most constraints. Third, the signal components may or may not be reconstructed depending on the noise level. As the noise power increases, we observe rank reduction in the optimal solution under most reasonable constraints. In these cases it appears that it is preferable to omit the smaller signal components rather than attempting to reconstruct them. This phenomenon has similarities with classical information theoretical results, notably the water-filling analogy, found in parallel additive Gaussian noise channels. Finally, we show that standard Hebbian-type PCA learning algorithms are not optimally robust to noise, and propose a new Hebbian-type learning algorithm which is optimally robust in the NPCA sense.

A new method for estimating 3D motion parameters from point correspondences is presented in this paper. The problem formulation leads to the solution of an overdetermined linear system of equations. The total least squares (TLS) method is found to be the most suitable one for estimating the solution since our model includes noise both in the observation data and in the system matrix. The translation parameters are obtained immediately from the above solution whereas the rotation parameters are estimated from the solution of another TLS problem. Tests of our method on artificial data and on real images show its robustness against Gaussian additive noise and against digitalization noise introduced by finite pixel resolution.

An efficient disparity estimation and occlusion detection algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to estimate disparity and to identify the occluded areas in the extreme left and right views. A significant advantage of the proposed approach is that the exact number of views in which each point appears (is not occluded) can be determined. The disparity and occlusion information obtained may then be used to create virtual images from intermediate viewpoints. Furthermore, techniques are developed for the coding of occlusion and disparity information, which is needed at the receiver for the reproduction of a multiview sequence using the two encoded extreme views. Experimental results illustrate the performance of the proposed techniques.

This paper describes a three-dimensional (3-D) model-based unsupervised procedure for the segmentation of multiview image sequences using multiple sources of information. The 3-D model is initialized by accurate adaptation of a two-dimensional wireframe model to the foreground object of one of the views. The articulation procedure is based on the homogeneity of parameters, such as rigid 3-D motion, color, and depth, estimated for each subobject, which consists of a number of interconnected triangles of the 3-D model. The rigid 3-D motion of each subobject for subsequent frames is estimated using a Kalman filtering algorithm, taking into account the temporal correlation between consecutive frames. Information from all cameras is combined during the formation of the equations for the rigid 3-D motion parameters. The threshold used in the object segmentation procedure is updated at each iteration using the histogram of the subobject parameters. The parameter estimation for each subobject and the 3-D model segmentation procedures are interleaved and repeated iteratively until a satisfactory object segmentation emerges. The performance of the resulting segmentation method is evaluated experimentally.

This paper presents an algorithm for the estimation of the motion of textured objects undergoing nonrigid deformations over a sequence of images. An active mesh model, which is a finite-element deformable membrane, is introduced in order to achieve efficient representation of global and local deformations. The mesh is constructed using an adaptive triangulation procedure that places more triangles over high detail areas. Through robust least squares techniques and modal analysis, efficient estimation of global object deformations is achieved, based on a set of sparse displacement measurements. A local warping procedure is then applied to minimize the intensity matching error between subsequent images, and thus estimate local deformations. Among the major contributions of this paper are novel techniques developed to acquire knowledge of the object dynamics and structure directly from the image sequence, even in the absence of prior intelligence regarding the scene. Specifically, a coarse-to-fine estimation scheme is first developed, which adapts the model to locally deforming features. Subsequently, principal components modal analysis is used to accumulate knowledge of the object dynamics. This knowledge is finally exploited to constrain the object deformation. The problem of tracking the model over time is addressed, and a novel motion-compensated prediction approach is proposed to facilitate this. A novel method for the determination of the dynamical principal axes of deformation is developed. The experimental results demonstrate the efficiency and robustness of the proposed scheme, which has many potential applications in the areas of image coding, image analysis, and computer graphics.

A rate-distortion framework is used to define a displacement vector field estimation technique for use in video coding. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector sequence. The technique may be adapted so as to limit its smoothing effect to homogeneous areas and avoid highly textured areas and edges. Use of this technique is evaluated for two application areas in which the need for high compression of displacement vector fields is particularly acute. The first is motion field coding for very-low-bit-rate image sequence transmission, as in video-phone applications. The second application area is coding for the transmission of dense disparity fields. This is needed for the generation at the receiver of intermediate viewpoints through spatial interpolation. It is also needed in a number of other applications requiring accurate depth knowledge, including three-dimensional medical data transmission and transmission of scenes to be postprocessed using depth-keyed segmentation. Experimental results illustrating the performance of the proposed technique in these application areas are presented and evaluated.

Optimal hierarchical coding is sought, for progressive or scalable image transmission, by minimizing the variance of the error difference between the original image and its lower resolution renditions. The optimal, according to the above criterion, pyramidal and subband image coders are determined for images subject to corruption by quantization or transmission noise. Given arbitrary analysis filters and assuming adequate knowledge of the noise statistics, optimal synthesis filters are found. The optimal analysis filters are subsequently determined, leading to formulas for globally optimal structures for pyramidal and subband image decompositions. Experimental results illustrate the implementation and performance of the optimal coders.

A new method is presented for the analysis of the effects of Lloyd-Max quantization in subband filterbanks and for the optimal design of such filterbanks. A rigorous statistical model of a vector Lloyd-Max quantizer is established first, consisting of a linear time-invariant filter followed by additive noise uncorrelated/with the input. On the basis of this model, an expression for this variance of the error of a subband coder using Lloyd-Max quantizers is explicitly determined. Given analysis filters that statistically separate the subbands, it is shown that this variance is minimized if the synthesis filters are chosen, which mould achieve perfect reconstruction in lossless coding. The globally optimum of such a filterbank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. An alternative design method is also evaluated and optimized. In this, the errors correlated with the signal are set to zero, leaving a random error residue uncorrelated with the signal. This design method is optimized by choosing the analysis filters so as to minimize the random error variance. The results are evaluated experimentally in the realistic setting of a logarithmically split subband image coding scheme.

In the present work, the nonlinear principal component analysis (NLPCA) method is combined with vector quantization for the coding of images. The NLPCA is realized using the backpropagation neural network (NN), while vector quantization is performed using the learning vector quantizer (LVQ) NN. The effects of quantization in the quality of the reconstructed images are then compensated by using a novel codebook vector optimization procedure.

The present paper describes methods for the design of both synchronous and asynchronous computer-supported cooperative work (CSCW) procedures suitable for the medical application area and specifically for the purpose of medical teleconsultation and remote diagnosis support. The experimental implementation of a CSCW system built upon a PC/Windows platform is detailed as an example of a low-cost system suitable for adoption in a wide range of medical teleconsultation applications.

The linear threshold element (LTE), or perceptron, is a linear classifier with limited capabilities due to the problems arising when the input pattern set is linearly nonseparable. Assuming that the patterns are presented in a sequential fashion, we derive a theory for the detection of linear nonseparability as soon as it appears in the pattern set. This theory is based on the precise determination of the solution region in the weight space with the help of a special set of vectors. For this region, called the solution cone, we present a recursive computation procedure which allows immediate detection of nonseparability. The separability-violating patterns may be skipped so that, at the end, we derive a totally separable subset of the original pattern set along with its solution cone. The intriguing aspect of this algorithm is that it can be directly cast into a simple neural-network implementation. In this model the synaptic weights are committed (they are updated only once, and the only change that may happen after that is their destruction). This bears resemblance to the behavior of biological neural networks, and it is a feature unlike those of most other artificial neural techniques. Finally, by combining many such neural models we develop a learning procedure capable of separating convex classes.

Noisy principal component analysis (NPCA) was introduced previously as an extension of PCA in the assumption that the linear features are unreliable. The level of noise in the representation variables is found to have effects in the rank of the optimal solution resembling the water-filling analogy in information theory. The NPCA problem needs to be coupled with certain constraints so that it permits a finite solution. We present the solution of the NPCA problem under different constraints which can be useful in applications involving bandwidth limitations. One of these applications is the design of optimal subband coders incorporating quantization noise. In addition to the NPCA-optimality another advantage of the new design approach is that it works entirely in the time domain and thus the costly and difficult transformations to and from the Z-domain can be avoided.

A coding method for videophone communications over wireless, varying bit-rate, channels, is proposed. This method is based on defining one or more areas of interest and breaking them into a number of arbitrarily shaped regions which are homogeneous in their motion. The interest areas in the current implementation are human faces. They are detected with the help of a 2D scene model which is a-priori constructed and is based on knowledge of the application context. A mixed intra/inter-frame coding method is used for the description of the shape and topology of the regions where each polygonal vertex is encoded in a costless manner, either with regards to its predecessor in the same frame, or to the corresponding vertex in the previous frame. This coding is based on rate/distortion optimization constrained by the instantaneously available bit-rate. The error coding is also performed in an adaptive prioritized manner and is restricted within the detected areas of interest.

This paper describes a procedure for model-based coding of all the channels of a multiview image sequence. The scheme is initialized by the adaptation of a wireframe model to the consistent depth information. Robust classification techniques are then used to obtain an articulated description of the foreground of the scene (head, neck, shoulders). The object articulation procedure is based on a novel scheme for the segmentation of the rigid 3D motion fields of the triangle patches of the 3D model object. Spatial neighborhood constraints are used to improve the reliability of the original triangle motion estimation. The motion estimation and motion field segmentation procedures are repeated iteratively until a satisfactory object articulation emerges. Rigid 3D motion estimation is performed next for each resulting sub-object. The performance of the resulting articulation method is evaluated experimentally.

The maturing of telecommunication technologies has ushered a whole new era of applications and services in the health care environment. Teleworking, teleconsultation, multimedia conferencing and medical data distribution are rapidly becoming commonplace in clinical practice. As a result, a set of problems arises, concerning data confidentiality and integrity. Public computer networks, such as the emerging ISDN technology, are vulnerable to eavesdropping. Therefore it is important for telemedicine applications to employ end-to-end encryption mechanisms securing the data channel from unauthorised access or modification. We propose a network access and encryption system that is both economical and easily implemented for integration in developing or existing applications, using well-known and thoroughly tested encryption algorithms. Public-key cryptography is used for session-key exchange, while symmetric algorithms are used for bulk encryption. Mechanisms for session-key generation and exchange are also provided.

Reduced pyramids, including in particular pyramids without analysis filters are known to produce excellent results when used for lossless signal and image compression. The present paper presents a methodology for the optimal construction of such pyramids by selecting the interpolation synthesis post-filters so as to minimize the error variance at each level of the pyramid. This establishes optimally efficient interpolative pyramidal lossless compression. It also has the added advantage of producing lossy replicas of the original which, at lower resolutions retain as much similarity to the original as possible. The general optimization methodology is developed first, for a general family of reduced pyramids. Subsequently, this is applied to the optimization of pyramids in this family formed using 2D quincunx sampling matrices. Optimal versions of these techniques are determined for 2D images characterized by separable or isotropic correlation functions. The advantages of the developed methods are demonstrated by experimental evaluation.

A rate-distortion framework is used to define a very low bit-rate coding scheme based on quadtree segmentation and optimized selection of motion estimators. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector field and segmentation information. First, a complete scheme is proposed for hybrid two-dimensional (2-D) and three-dimensional (3-D) motion estimation and compensation. The quadtree object segmentation is optimized for hybrid motion estimation in the rate-distortion sense. This scheme adapts to the depth of the quadtree and the technique used for motion estimation for each leaf of the tree. A more sophisticated technique, adapted to the requirements of a very low bit-rate coder, is also proposed which also considers the transmission of the prediction error corresponding to the particular choice of the motion estimator. Based on these coding schemes, two versions of a very low bit-rate image sequence coder are developed. Experimental results illustrating the performance of the proposed techniques in very low bit-rate image sequence coding application areas are presented and evaluated.

Maximum likelihood (ML) techniques are defined for optimum block matching to enable motion estimation sequences of ultrasound B-mode images. Such motion estimation is needed as a diagnostic tool in medical use of ultrasound imagery. It is also needed for the efficient compression of sequences of ultrasound images. The novel ML block matching techniques correspond to accurate statistical descriptions of ultrasound images, and are evaluated experimentally using sequences of transesophageal ultrasound images of the heart.

In this paper, a motion estimation algorithm is presented, based on spatiotemporal warping of an image sequence. The motion field function is modeled by isoparametric cubic finite elements, depending on a set of elementary displacement vectors. A model refinement scheme is proposed for the adaptively higher bit allocation to motion information in locations with high-motion activity.

An object-based stereo image coding algorithm is proposed. The algorithm relies on modeling of the object structure using 3-D wire-frame models and motion estimation using globally rigid and locally deformable motion models. Algorithms for the estimation of motion and structure parameters from stereo images are described. Motion parameters are used to construct predicted images by mapping the image texture on the object surface. Coding of object parameters, appearing background regions, and prediction errors are investigated, and experimental results with standard stereo image sequences depicting general scenes are presented. The proposed algorithm is seen to be very efficient for applications such as stereoscopic video transmission. Furthermore, it is especially well suited to advanced applications such as generation and transmission of intermediate views for multiview receiver systems and applications in which object-wise editing of the bit stream is required. The latter include video production using preanalyzed scenes and virtual reality applications.

An object-based coding scheme is proposed for the coding of a stereoscopic image sequence using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split-and-merge segmentation procedure based on three-dimensional (3-D) motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion-compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using discrete cosine transform (DCT) and Huffman methods. Alternately, an efficient wireframe depth modeling technique may be used to convey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.

We present a very simple solution of the motion recovery problem using the displacement map produced by a camera moving in a stationary environment under the perspective projection assumption. If the motion equations hold exactly (ideal case), the solution is derived immediately from the singular value decomposition (SVD) of a linear system matrix. In the ideal case the rank of the matrix indicates whether there is a zero or non-zero translation component in the camera motion. In practice the motion equations do not hold exactly. We speculate that the solution vector is a linear combination of the 3 smallest but non-zero eigenvalue eigenvectors. Then we find the 3 combination coefficients so that the solution obeys an equal number of problem constraints. The speculation turns out to yield good results for small noise situations but degrades fast as the noise power increases.

Hierarchical prioritized predictive image coding methods are presented for progressive image transmission. The three main coder stages are hierarchical transform, prioritized coefficient coding and adaptive multiple distribution entropy coding (AMDEC). Following a hierarchical subband decomposition of the original image, the quantized coefficients are scalar quantized and then coded using novel hierarchical partition priority coding (HPPC) and predictive HPPC (PHPPC) algorithms. Given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and, then, are DPCM coded along with the corresponding address map. The use of space filling scanning further reduces the coding cost. Finally, AMDEC is applied to the HPPC/PHPPC output. Experimental results demonstrate the performance of the proposed compression methods.

An efficient disparity estimation and occlusion detection and characterization algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. An important advantage of this approach is that not only are the occluded points simultaneously detected, but they are also characterized by the number of views where each point is occluded. Specifically, a "state" map describing the number of matches for each imaged pixel and thus identifying occluded points in the multiview sequence is produced. The disparity and state information is then applied to obtain virtual images from intermediate viewpoints. Experimental results, obtained using a four-view image sequence, illustrate the performance of the proposed technique.

A new method is presented for the analysis of the effects of Lloyd-Max quantization in subband filter banks and for the optimal design of such filter banks. A rigorous statistical model of a vector Lloyd-Max quantizer is established first, consisting of a linear time-invariant filter followed by additive noise uncorrelated with the input. On the basis of this model, an expression for this variance of the error of a subband coder using Lloyd-Max quantizers is explicitly determined. Given analysis filters which statistically separate the subbands, it is shown that this variance is minimized if the synthesis filters are chosen, which would achieve perfect reconstruction in lossless coding. The globally optimum filter bank, minimizing the coder error variance, is further obtained by proper choice of its analysis filters. The results are evaluated experimentally in the realistic setting of a logarithmically split subband image coding scheme.

Optimal hierarchical coding is sought, for progressive or scalable image transmission, by minimizing the variance of the error difference between the original image and its lower resolution renditions. The optimal, according to the above criterion, pyramidal image coders are determined for images subject to corruption by quantization or transmission noise. Given arbitrary analysis filters and assuming adequate knowledge of the noise statistics, the optimal synthesis filters are found. The optimal analysis filters are subsequently determined, leading to formulas for globally optimal structures for pyramidal and subband image decompositions. Experimental results illustrate the implementation and performance of the optimal coders.

Methods are proposed for coding of the depth map and disparity fields for stereo or multiview image communication applications. Block-based and wireframe modeling techniques are examined for the coding of isolated depth map information and 2-D and 3-D motion compensation techniques for the coding of depth map sequences are evaluated.

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) method is applied to the smoothed subband coefficients, and the corresponding prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficients in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms. More specifically, given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map. Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms. Experimental results demonstrate the great performance of the proposed compression methods.

Motion vector field (MVF) prediction methods are presented followed by a restoration method. These methods combined with a proposed motion compensated (MC) video coding scheme are suitable for low bit rate transmission. An expression is derived for the initial estimate of the working MVF based on the preceding MVF. Spatio-temporally adaptive regularization is applied using neighborhood information. The output MVF is used as the initial prediction estimate for a Kalman MVF restoration approach. By applying this method to both the encoder and decoder, the resulting MC MVF and image intensity temporal updates are coded and transmitted. The restoration method produces accurate estimates of the MVF, thus resulting in a significant transmission cost reduction. Experiments with standard video-conference image sequences demonstrate the improved performance of the proposed scheme.

Temporal frame interpolation techniques are presented, based on an object-based algorithm for 3-D motion estimation. This algorithm uses a joint estimation-segmentation scheme to minimize the displaced frame difference between a frame and its motion compensated prediction from the previous frame. Depth information is estimated beforehand from each stereo pair. Special attention is paid to the exploitation of occlusion information so as to improve the reconstruction quality of the interpolated frames. Experimental results are used to evaluate the performance of the proposed methods and to compare with more conventional frame interpolation techniques.

The selection is considered of scaling functions for optimal signal representation by multidimensional biorthogonal wavelet frames. Criterion for optimality is the minimization of the mean-square approximation error at each level of the decomposition. Conditions are given under which the approximation error of the decomposition approaches zero as the level increases. Optimal and suboptimal families of filters for the realization of the scaling functions are explicitly defined.>

Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L(2) mean which has been proven earlier to be the ML estimator of the original signal in US B-mode data. Thus, the design of signal-adaptive L(2) mean filters is treated for US B-mode data and displayed US image data as well. Secondly, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of the learning vector quantizer (L(2 ) LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L(2) mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed L(2) LVQ NN are studied. L(2) LVQ is combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions.

Fast computation algorithms are developed for twodimensional and general multidimensional convolutions. Two basic techniques (overlap-and-add, overlap-and-save) are described in detail. These techniques allow speed and storage requirement tradeoffs and they define a decomposition of the total convolution into partial convolutions that can be easily found by parallel use of fast sequential cyclic convolution algorithms. It is shown that unlike what is the case in one dimension, the ``overlap-and-save'' method enjoys a clear advantage over the ``overlap-and-add'' method with respect to speed and storage in multidimensional convolution. A specific computational burden is assessed for the case where these methods are used in conjunction with radix-2 fast Fourier transform algorithms.

This paper considers a detection theory approach to the restoration of digitized images. The images are modeled as second-order Markov meshes. This model is not only well suited to a decision approach to smoothing, but it enables computer simulations of images thereby permitting a statistical analysis of restoration techniques. Smoothing procedures that are near optimal in the sense of approaching a nonrealizable bound are demonstrated and evaluated. The achievable reduction in mean-square error is considerable for coarsely quantized pictures. This reduction, for the four-level pictures considered, is somewhat greater than that achievable by linear techniques. The approach actually minimizes the probability of error which may be important for preserving picture features.

The spectral factorization of para-Hermitian matrices is often required in problems of filtering theory, network synthesis, and control systems design. An algebraic factorization technique is developed. The factorization of the matrix\Phi(s)is shown to be directly determined by the unique solution of a system of linear matrix equations. The labor of the method is reduced when some forms of prefactorization of\Phi(s)are available. Furthermore, the calculation is extremely simple for the case, common in practice, that\Phiis given in the formH(s) H^T (-s)withHanalytic in the open right half-plane. A technique is given for prefactoring an arbitrary\Phi(s)into this form.