The widespread adoption of Unmanned Aerial Vehicles (UAVs) has raised critical security and safety concerns, particularly in sensitive areas and air traffic management. Modern counter-drone systems integrate multiple sensing modalities, but their development is hindered by the lack of comprehensive, publicly available datasets. To address this, the Drone-vs-Bird Detection Grand Challenge provides a manually annotated UAV dataset to advance research in drone detection. Since its inception in 2017, the competition has attracted global interest, fostering the development of advanced detection methods. This paper presents an overview of the 8th edition as data competition hosted at the International Joint Conference on Neural Networks (IJCNN) 2025. The data competition generated high engagement with 16 competing algorithms successfully submitted. The variability of the results underscores the complexity of the task and the need for future research. Over almost a decade, this data competition has been bridging the domains of signal processing, computer vision, and deep learning, paving the way for next-generation counter-drone solutions.

Abstract The proliferation of deepfake technology poses significant challenges due to its potential for misuse in creating highly convincing manipulated videos. Deep learning (DL) techniques have emerged as powerful tools for analyzing and identifying subtle inconsistencies that distinguish genuine content from deepfakes. This paper introduces a novel approach for video deepfake detection that integrates 3D Morphable Models (3DMMs) with a hybrid CNN-LSTM-Transformer model, aimed at enhancing detection accuracy and efficiency. Our model leverages 3DMMs for detailed facial feature extraction, a CNN for fine-grained spatial analysis, an LSTM for short-term temporal dynamics, and a Transformer for capturing long-term dependencies in sequential data. This architecture effectively addresses critical challenges in current detection systems by handling both local and global temporal information. The proposed model employs an identity verification approach, comparing test videos with reference videos containing genuine footage of the individuals. Trained and validated on the VoxCeleb2 dataset, with further testing on three additional datasets, our model demonstrates superior performance to existing state-of-the-art methods, maintaining robustness across different video qualities, compression levels and manipulation types. Additionally, it operates efficiently in time-sensitive scenarios, significantly outperforming existing methods in inference speed. By relying solely on pristine, unmanipulated data for training, our approach enhances adaptability to new and sophisticated manipulations, setting a new benchmark for video deepfake detection technologies. This study not only advances the framework for detecting deepfakes but also underscores its potential for practical deployment in areas critical for digital forensics and media integrity.

This paper presents the 6th edition of the "Drone-vs-Bird" Detection Grand Challenge, organized within the 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Taking video samples recorded by commercial RGB cameras as input, the challenge stimulates the design of advanced approaches capable of detecting the presence of small drones flying in a given area under surveillance. Successful methods should ensure high detection rates while limiting the number of false alarms erroneously triggered in presence of very similar false targets (birds). The paper summarizes the novel methods proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2023 challenge dataset.

This paper presents the 4-th edition of the “drone-vs-bird” detection challenge, launched in conjunction with the the 17-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The objective of the challenge is to tackle the problem of detecting the presence of one or more drones in video scenes where birds may suddenly appear, taking into account some important effects such as the background and foreground motion. The proposed solutions should identify and localize drones in the scene only when they are actually present, without being confused by the presence of birds and the dynamic nature of the captured scenes. The paper illustrates the results of the challenge on the 2021 dataset, which has been further extended compared to the previous edition run in 2020.

In the field of 3D Human Performance Capture, a high-quality 3D scan of the performer is rigged and skinned to an animatable 3D template mesh that is subsequently fitted to the captured performance's RGB-D data. Template fitting is accomplished via solving for the template's pose parameters that better explain the performance data at each recorded frame. In this paper, we challenge open implementations of zeroth-order optimizers to solve the template fitting problem in a human performance capture dataset. The objective function that we employ approximates, the otherwise costly to evaluate, 3D RMS hausdorff distance between the animated template and the 3D mesh reconstructed from the depth data (target mesh) at an individual recorded frame. We distinguish and benchmark the optimizers, in three different real-world scenarios, two of which are based on the geometric proximity of the template to the target in individual frames, while in the third one we fit the template sequentially to all target frames of the recorded sequence. Conclusions of this work can serve as a reference for future optimizer implementations and our findings can server as a baseline for future multi-objective optimization approaches. We make part of our benchmark and experiment setup publicly available (https://github.com/VCL3D/nevergrad, https://github.com/VCL3D/PerformanceCapture/releases/).

Communication with a customer or future user during the planning and design phase is crucial in applications such as interior design and furniture retailing. Augmented Reality (AR) has the potential to make these communication processes highly effective and provide a better experience for the customer. Current AR authoring solutions are quite complex and require manually creating scenes or rely on objects prepared with even more complex applications such as CAD tools. However, both design experts and their customers often lack the IT skills to use these tools. In addition, many practical cases involve changing reality rather than just adding to it, thus requiring the use of Diminished Reality (DR) technologies. This paper presents a comprehensive analysis of the requirements of both professionals and consumers (gathered using user surveys and individual interviews) for a lightweight and automated authoring process of AR and DR experiences, deriving a set of requirements that can be aligned with state of the art technologies and identifying a number of challenges for AR research.

It has been shown that global scene understanding tasks like layout estimation can benefit from wider field of views, and specifically spherical panoramas. While much progress has been made recently, all previous approaches rely on intermediate representations and postprocessing to produce Manhattan-aligned estimates. In this work we show how to estimate full room layouts in a single-shot, eliminating the need for postprocessing. Our work is the first to directly infer Manhattan-aligned outputs. To achieve this, our data-driven model exploits direct coordinate regression and is supervised end-to-end. As a result, we can explicitly add quasi-Manhattan constraints, which set the necessary conditions for a homography-based Manhattan alignment module. Finally, we introduce the geodesic heatmaps and loss and a boundary-aware center of mass calculation that facilitate higher quality keypoint estimation in the spherical domain. Our models and code are publicly available at https://github.com/VCL3D/SingleShotCuboids.

With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension on the practicality of spatially aligning multiple sensors. Most existing alignment approaches employ visual patterns, e.g. checkerboards, or markers and require high user involvement and technical knowledge. More user-friendly and easier-to-use approaches rely on markerless methods that exploit geometric patterns of a physical structure. However, current SoA approaches are bounded by restrictions in the placement and the number of sensors. In this work, we improve markerless data-driven correspondence estimation to achieve more robust and flexible multi-sensor spatial alignment. In particular, we incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. This is accomplished by a soft, differentiable procrustes analysis that regularizes the segmentation and achieves higher extrinsic calibration performance in expanded sensor placement configurations, while being unrestricted by the number of sensors of the volumetric capture system. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the mark-erless ones, while also being robust to the pose variations of the calibration structure. Code and pretrained models are available at https://vcl3d.github.io/StructureNet/.

Omnidirectional vision is becoming increasingly relevant as more efficient 360° image acquisition is now possible. However, the lack of annotated 360° datasets has hindered the application of deep learning techniques on spherical content. This is further exaggerated on tasks where ground truth acquisition is difficult, such as monocular surface estimation. While recent research approaches on the 2D domain overcome this challenge by relying on generating normals from depth cues using RGB-D sensors, this is very difficult to apply on the spherical domain. In this work, we address the unavailability of sufficient 360° ground truth normal data, by leveraging existing 3D datasets and remodelling them via rendering. We present a dataset of 360° images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360° surface estimation. We achieve this by minimizing a novel angular loss function defined on the hyper-sphere using simple quaternion algebra. We put an effort to appropriately compare with other state of the art methods trained on planar datasets and finally, present the practical applicability of our trained model on a spherical image re-lighting task using completely unseen data by qualitatively showing the promising generalization ability of our dataset and model.

Learning based approaches for depth perception are limited by the availability of clean training data. This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures. Nonetheless, most research focuses on pinhole based monocular vision, with scarce works presenting results for omnidirectional input. In this work, we explore spherical view synthesis for learning monocular 360 depth in a self-supervised manner and demonstrate its feasibility. Under a purely geometrically derived formulation we present results for horizontal and vertical baselines, as well as for the trinocular case. Further, we show how to better exploit the expressiveness of traditional CNNs when applied to the equirectangular domain in an efficient manner. Finally, given the availability of ground truth depth data, our work is uniquely positioned to compare view synthesis against direct supervision in a consistent and fair manner. The results indicate that alternative research directions might be better suited to enable higher quality depth perception. Our data, models and code are publicly available at https://vcl3d.github.io/SphericalViewSynthesis/.

This paper presents the second edition of the “drone-vs-bird” detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.

The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can “see” an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.

Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce self-supervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/VCL3D/DeepDepthDenoising.

Geometry and texture resolution are two common system parameters of any modern volumetric 3D reconstruction pipeline. In tele-immersive applications, besides their apparent impact on the visual quality of the output 3D mesh, their absolute values implicitly influence the computational load of the whole tele-immersion pipeline from acquisition to 3D reconstruction, compression and transmission. Thus, tuning those parameters to an optimal combination has evident benefits. In this paper, we conduct a subjective experiment to assess the visual quality of textured human 3D-reconstructed meshes that are produced by a volumetric 3D reconstruction algorithm as a joint function of the geometry and texture resolution production parameters. The experiment is based on the forced choice pairwise comparison methodology on pre-rendered views of the real-time reconstructed meshes within the context of human performance capture. We analyze the pairwise comparison data and establish a ranking of the parameter space and, thus also, a mapping from the parameters to the subjective visual quality. The results of this study may be utilized to tune the parameters of the real-time 3D reconstruction pipeline, optimizing for the best balance between visual quality, bandwidth and overall performance.

Traditional VR is mostly about headset experiences either in completely virtual environments or 360° videos. On the other hand AR has been mixing realities by inserting the virtual within the real. In this work we present the Augmented VR concept that lies at the middle right of the virtuality continuum, typically referred to as augmented virtuality. We offer another perspective by blending the real within the virtual focusing on capturing actual human performances in three dimensions and emplacing them within virtual environments [1]-[3]. By compressing and transmitting this new type of 3D media we can also achieve real-time interaction, communication and collaboration between users. Being in full 3D our media are compatible with a variety of applications be it either VR, AR, MR and open up new exciting opportunities like free viewpoint spectating while also increasing the feeling of immersion of all participating users. We demonstrate our technology via a prototype two player game that can support spectating in various devices like head mounted displays (VR) or tablet laptops (AR). Our system is easy to setup, requiring minimal non-technical human intervention, and relatively low cost taking one step ahead in making this technology available to the consumer public.

Cardiac Rehabilitation (CR) can significantly improve mortality and morbidity rates from Cardiovascular Diseases (CVD). Nevertheless, traditional CR is diminished by low subsequent adherence rates. Thus, in this paper, an e-Health technological module for human motion analysis and user modelling is proposed, in order to address the requirements of unsupervised, tele-rehabilitation systems for CVD, by evaluating and personalizing prescribed physical CR programs. The proposed module consists of a) an exercise capturing and evaluation component, and b) a user modelling and decision support system for personalization of cardiac rehabilitation programs. In particular, the module monitors and analyses the body movements of the patient when exercising in real-time, while based on this analysis and the heart-rate measurements, it is capable of short-term and long-term CR session adaptation. The proposed module constitutes a significant tool for internet-enabled sensor-based home exercise platforms.

Camera pose estimation is a fundamental problem of Augmented Reality and 3D reconstruction systems. Recently, despite the new better performing direct methods being developed, state-of-the-art methods are still estimating erroneous poses due to sensor noise, environmental conditions and challenging trajectories. Adding a back-end mapping process, SLAM systems achieve better performance and are more robust, but require higher computational resources, limiting their applicability. Therefore, lighter solutions to improve the accuracy of pose estimates are required. In this work we demonstrate the effectiveness of lighter data structures, namely surface elements, and exploit the temporality of sensor data streams to accumulate moving camera frames and improve tracking. This representation allows us to splat a photometric and geometric model simultaneously and use it to improve the performance of dense RGB-D camera pose estimation methods. Exploiting Elliptical Weighted Average splatting to produce high quality photometric results also allows us to detect erroneous poses through a novel visual quality analysis process. We show evidence of the EWA temporal model's effectiveness in publicly available datasets and argue that point-based representations are a good candidate for building lighter systems that should be further explored.

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways. The main elements of an integrated platform, which target tele-immersion and future 3D applications, are described in this paper, addressing the tasks of real-time capturing, robust 3D human shape/appearance reconstruction, and skeleton-based motion tracking. More specifically, initially, the details of a multiple RGB-depth (RGB-D) capturing system are given, along with a novel sensors' calibration method. A robust, fast reconstruction method from multiple RGB-D streams is then proposed, based on an enhanced variation of the volumetric Fourier transform-based method, parallelized on the Graphics Processing Unit, and accompanied with an appropriate texture-mapping algorithm. On top of that, given the lack of relevant objective evaluation methods, a novel framework is proposed for the quantitative evaluation of real-time 3D reconstruction systems. Finally, a generic, multiple depth stream-based method for accurate real-time human skeleton tracking is proposed. Detailed experimental results with multi-Kinect2 data sets verify the validity of our arguments and the effectiveness of the proposed system and methodologies.

In this paper, we propose a novel, full-body, real-time 3D reconstruction framework that makes use of pre-scanned body parts (more precisely pre-scanned 3D heads) so as to provide a more detailed 3D reconstruction mainly in the semantically important head area. Our framework deals with 3 major challenges: (a) multiple depth sensors collaboration, (b) pre-scanned head positioning and (c) reconstruction and texturing. In all the above challenges, we propose novel solutions so as to cope with time and space complexity, synchronization and 3D mesh quality. Experimental evaluation provides evidence that superior 3D mesh quality can be achieved compared to simple (not enhanced) use of depth cameras' data.

Tele-immersion (TI) related technologies can change the way people interact and bridge the gap between the physical and digital worlds. However, while the technology itself advances, most developed platforms have complex setups and require large investments. In this work, a low-cost platform is introduced, integrating multiple TI-related advances. Focusing on ease of use and rapid deployment, a fast and fully automatic calibration method is proposed. The platform enables real-time 3D reconstruction of users and their placement into a pre-authored 3D environment. Moreover, interaction is achieved through the user's body posture, removing the need for additional equipment and enabling natural control while immersed. Developing a real-time TI platform requires the efficient integration of several multidisciplinary elements. An elegant, minimal solution to these challenges is proposed and validated in a prototype TI multiplayer game, SpaceWars.

We present a demonstration of a multi-modal 3D capturing platform coupled to a motion comparison system. This work is focused on the preservation of Traditional Sports and Games, namely the Gaelic sports from Ireland and Basque sports from France and Spain. Users can learn, compare and compete in the performance of sporting gestures and compare themselves to real athletes. Our online gesture database provides a way to preserve and display a wide range of sporting gestures. The capturing devices utilised are Kinect 2 sensors and wearable inertial sensors, where the number required varies based on the requested scenario. The fusion of these two capture modalities, coupled to our inverse kinematic algorithm, allow us to synthesize a fluid and reliable 3D model of the user gestures over time. Our novel comparison algorithms provide the user with a performance score and a set of comparison curves (i.e. joint angles and angular velocities), providing a precise and valuable feedback for coaches and players.

We present a novel, low-cost, interactive, exercise-based rehabilitation system. Our research involves the investigation and development of patient-centric, sensor-based rehabilitation games and surrounding technologies. HeartHealth is designed to provide a safe, personalised and fun exercise environment that could be deployed in any exercise based rehabilitation program. HeartHealth utilises a cloud-based patient information management system built on FIWARE Generic Enablers,and motion tracking coupled with our sophisticated motion comparison algorithms. Users can record customised exercises through a doctors interface and then play the rehabilitation game where they must perform a sequence of their exercises in order to complete the game scenario. Their exercises are monitored, recorded and compared by our Motion Evaluation software and real-time feedback is than given based on the users performance.

In this work, we explore the potential of exploiting activity-related global features in order to improve the performance of an existing human Time-Varying Mesh (TVM) compression scheme. The TVM compression scheme used, employs two kinds of frames, namely Intra(I)-Fames and Enhanced Predicted(EP) Frames. In this scheme, I-Frames are used as a reference to encode EP-Frames. The paper introduces a strategy for selecting the most appropriate I-Frame that will serve as a reference frame for the encoding of EP-Frames, exploiting activity-related characteristics. Two different strategies are presented, using a skeleton-matching criterion and a periodicity measurement metric based on human skeleton. Evaluation is conducted on two sequences of the MPEG-3DGC database [1]. Results show that the concept is sound, but they also reveal the sensitivity of the proposed methods to the skeleton quality, thus the need for more robust skeleton tracking techniques.

The primary objective of this paper is to present and analyze key aspects related to next-generation tele-immersion applications, studying the end-to-end chain from 3D capturing of remote users to rendering. The key modules for 3D reconstruction of moving humans and their mesh compression, are presented and discussed. The chain performance is evaluated in terms of frame-rates, delay, and visual quality.

In this paper, the problem of real-time, full 3D reconstruction of foreground moving objects, an important task for Tele-Immersion applications, is addressed. More specifically, the proposed reconstruction method receives input from multiple consumer RGB-Depth cameras. A fast and efficient method to calibrate the sensors in initially described. More importantly, an efficient method to smoothly fuse the captured raw point sets is then presented, followed by a volumetric method to produce watertight and manifold meshes. Given the implementation details, the proposed method can operate at high frame rates. The experimental results, with respect to reconstruction quality and rates, verify the effectiveness of the proposed methodology.

In this paper, a novel skeleton-based approach to human time-varying mesh (H-TVM) compression is presented. The topic of TVM compression is new and has many challenges, such as handling the lack of obvious mapping of vertices across frames and handling the variable connectivity across frames, while maintaining efficiency, which are the most important ones. Very few works exist in the literature, while not all of the challenges have been addressed yet. In addition, developing an efficient and real-time solution, handling the above, obviously is a difficult task. We attempt to address the H-TVM compression problem inspired from video coding using different types of frames and trying to efficiently remove inter-frame geometric redundancy utilizing the recent advances in human skeleton tracking. The overall approach focuses on compression efficiency, low distortion, and low computation time enabling for real-time transmission of H-TVMs. It efficiently compresses geometry and vertex attributes of TVMs. In addition, this paper is the first to provide an efficient method for connectivity coding of TVMs, by introducing a modification to the state-of-the-art MPEG-4 TFAN algorithm. Experiments are conducted in the MPEG-3DGC TVM database. The method outperforms the state-of-the-art standardized static mesh coder MPEG-4 TFAN at low bit-rates, while remaining competent at high bit-rates. It gives a practical proof of concept that in the combined problem of geometry, connectivity, and vertex attribute coding of TVMs, efficient inter-frame redundancy removal is possible, establishing ground for further improvements. Finally, this paper proposes a method for motion-based coding of H-TVMs that can further enhance the overall experience when H-TVM compression is used in a tele-immersion scenario.

The problem of robust, realistic and especially fast 3-D reconstruction of objects, although extensively studied, is still a challenging research task. Most of the state-of-the-art approaches that target real-time applications, such as immersive reality, address mainly the problem of synthesizing intermediate views for given view-points, rather than generating a single complete 3-D surface. In this paper, we present a multiple-Kinect capturing system and a novel methodology for the creation of accurate, realistic, full 3-D reconstructions of moving foreground objects, e.g., humans, to be exploited in real-time applications. The proposed method generates multiple textured meshes from multiple RGB-Depth streams, applies a coarse-to-fine registration algorithm and finally merges the separate meshes into a single 3-D surface. Although the Kinect sensor has attracted the attention of many researchers and home enthusiasts and has already appeared in many applications over the Internet, none of the already presented works can produce full 3-D models of moving objects from multiple Kinect streams in real-time. We present the capturing setup, the methodology for its calibration and the details of the proposed algorithm for real-time fusion of multiple meshes. The presented experimental results verify the effectiveness of the approach with respect to the 3-D reconstruction quality, as well as the achieved frame rates.

Semantic wikis have been widely adopted to support a variety of collaborative activities within the health domain [6], [7], [9]. In this paper, relevant existing tools that may be taken into account for the development of a Wiki-based tool are revisited. The paper then proposes a collaborative Wiki-based tool to be used for semantic management and classification of unstructured and semi-structured medical interventions [12] spread across the Web. The architecture of the tool and its functionality are described in the light of some evidence and a discussion on how this tool may become useful in the semantic Web description of elderly care interventions in the ageing society.

Active Contour Models have been widely used in computer vision for segmentation purposes, while anatomically constrained ACMs have offered a valuable solution on medical image segmentation. Efforts have been devoted on various ways of modeling prior knowledge. This paper focuses on how to efficiently incorporate prior knowledge, into an ACM evolution framework, using the structures' distribution map as a second feature image, and blending the two images through a novel adaptive local weighting scheme. For proof of concept the method is applied on hippocampus segmentation in T1-MR brain images, a very challenging task, due to its multivariate surrounding region and the weak, even missing boundaries.

This paper presents a novel descriptor for recognizing objects in highly occluded and cluttered 2.5D scenes produced by range scans. This new compact regional shape descriptor, called "projection images", is designed to be robust against noise, partial occlusion and clutter. Projection images are formed by "projections" of points onto the plane centered at the basis point which is perpendicular to the viewing axis. Multiple experiments were performed on a dataset of 50 range scans, each one comprised of multiple objects, which proved that the proposed method is robust and efficient to a satisfactory degree of occlusion and clutter, while it compared favor- ably against descriptors previously introduced in the literature.

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 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.

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.

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.

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.

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.