In order to understand olfactory coding and functional connectome within the fly brain, scientists need not only a 3D stereotypical brain atlas but also anatomical local landmarks to describe hard-wiring circuits of olfactory neurons. Olfactory glomeruli are such indispensable local landmarks; however, it is hard to segment them from confocal microscopy images. We propose in this paper a systematic approach for semi-automatic olfactory glomerulus segmentation. This method consists of two phases. The former phase aims to highlight within-glomerulus regions, appraise the amount of recognizable glomeruli in each z-slice, and to suggest a seed used to generate an initial contour for every recognizable glomerulus. The latter phase, starting from the initial contour of each recognizable glomerulus, propagates the initial contour information to adjacent slices recursively until the glomerulus' contours on all z-slices are obtained. Experiment results shows that our results are similar to those of manual segmentation. The proposed strategy is effective.

Multi-resolution and wavelet analysis have generated considerable interest in the field of mesh surface representation. In this paper, we propose a backward, coarse-to-fine framework that derives a semi-regular approximation of an original mesh, and demonstrate its effectiveness on level-of-detail and scalable coding applications. The framework is flexible and simple because the position of a new vertex at a finer resolution can be derived in a closed form, based on the affine combination of a subdivision scheme, the original mesh, and “new” information about the wavelet coefficients. We report the results of experiments on both applications; and also compare the scalable coding results with those of other methods.

This paper proposes a hybrid sender/receiver-driven error protection scheme to transmit scalable video packets over packet-lossy peer-to-peer networks. In our scheme, given an estimated system uplink capacity, a joint source-channel coding (JSCC) mechanism based on receiver-driven subscriptions is proposed to minimize the visual distortion received by child-peers by subscribing to appropriate amounts of source and channel coding packets. Because the bandwidth for inter-peer transmissions may fluctuate largely due to peer dynamics, in our method peers estimate the available system uplink capacity based on consensus propagation to avoid the fluctuating allocations of JSCC. To efficiently utilize the uplink bandwidth of peers, parent-peers use sender-driven contribution-guided peer selection to reject the low-contribution subscriptions requested from candidate child-peers. Simulation results demonstrate that our method significantly improves visual quality, compared to other state-of-the-art schemes.

With the progress of model averaging algorithms, scientists in the field of brain research have an increasing demand for methods capable to register and warp source data to the pre-registered standard atlas. We here propose a thin-plate spline (TPS) based surface registration method to facilitate the registration and warping process of Drosophila brain data. Our contributions are twofold. First, the proposed method performs TPS-based registration in the parameterization domain, and hence it no longer needs a rigid transformation to globally align and scale the input models. Second, the obtained well-registered surface model can act as boundary constraints for further volumetric registration schemes. Experiments show that the proposed method is effective. For models with a 750-voxel-long bounding box diagonal, the average surface-to-surface distance is reduced to about 0.1-voxel-long after registration.

This paper presents an evolution-based hierarchical feature fusion system that selects the dominant features among multiple feature vectors for ultrasonic liver tissue characterization. After extracting the spatial gray-level dependence matrices, multiresolution fractal feature vectors and multiresolution energy feature vectors, the system utilizes evolution-based algorithms to select features. In each feature space, features are selected independently to compile a feature subset. As the features of different feature vectors contain complementary information, a feature fusion process is used to combine the subsets generated from different vectors. Features are then selected from the fused feature vector to form a fused feature subset. The selected features are used to classify ultrasonic images of liver tissue into three classes: hepatoma, cirrhosis, and normal liver. Experiment results show that the classification accuracy of the fused feature subset is superior to that derived by using individual feature subsets. Moreover, the findings demonstrate that the proposed algorithm is capable of selecting discriminative features among multiple feature vectors to facilitate the early detection of hepatoma and cirrhosis via ultrasonic liver imaging.

Recently developed were the Brainbow and Flybow techniques that can image and visualize a large number of neurons simultaneously; however, scientists still lack adequate tools to process this kind of colored multi-neuron image volumes. Due to dozens of colorized neuron fibers spreading densely in a very intricate structure, it is difficult to trace them by existing algorithms designed for single-neuron images. We proposed a framework to formulate and solve this issue, and the experimental results show that our method can successfully extract independent neurons from Flybow images. Consequently, the proposed procedure contributes to neuroscience by increasing the efficiency of collecting neuron information from Flybow images.

Feature extraction is an essential part in applications that require computer vision to recognize objects in an image processed. To extract the features robustly, feature extraction algorithms are often very demanding in computation so that the performance achieved by pure software is far from real-time. Among those feature extraction algorithms, scale-invariant feature transform (SIFT) has gained a lot of popularity recently. In this paper, we propose an all-hardware SIFT accelerator-the fastest of its kind to our knowledge. It consists of two interactive hardware components, one for key point identification, and the other for feature descriptor generation. We successfully developed a segment buffer scheme that could not only feed data to the computing modules in a data-streaming manner, but also reduce about 50% memory requirement than a previous work. With a parallel architecture incorporating a three-stage pipeline, the processing time of the key point identification is only 3.4 ms for one video graphics array (VGA) image. Taking also into account the feature descriptor generation part, the overall SIFT processing time for a VGA image can be kept within 33 ms (to support real-time operation) when the number of feature points to be extracted is fewer than 890.

This paper proposes a sender-driven peer selection scheme, including estimation of packet loss propagation, evaluation of peers' contributions, and peer selection based on child-peers' contributions, for mesh-based peer-to-peer (P2P) video streaming systems. The proposed packet loss propagation model takes into account the link packet drop rate, peer dynamics, and forward error correction protection to capture the heterogeneous packet loss behavior of individual substreams transmitted over a mesh network. The evaluation of candidate peers' contributions is modeled through Markov random fields to significantly reduce complexity. Simulation results demonstrate that our peer selection scheme significantly mitigates packet loss in a mesh-based P2P network, compared to other state-of-the-art schemes.

This paper proposes a distributed packet protection mechanism that can minimize the packet loss probability for mesh based P2P video streaming systems. The proposed scheme combines a peer selection method with forward error correction (FEC) codes. The parent peers select the child peers, which can achieve the minimal packet loss probability compared to other candidate child peers, to transmit the FEC redundant substream. Moreover, the proposed scheme utilizes a packet loss model to estimate the packet loss probability in a mesh based P2P network. The packet loss propagation among peers is modeled through Markov random field (MRF). Simulation results demonstrate that our scheme can effectively mitigate packet loss in a mesh-based P2P network.

This paper proposes a model to estimate the packet loss probability in a mesh-based P2P network. Because of the irregular mesh structure, packet loss estimation for a mesh-based P2P network is more complicated than that in a tree-based network. The proposed model takes into account the channel packet drop rate, peer dynamics, and FEC protection to capture the heterogeneous packet loss behavior of individual video substreams transmitted over the irregular transmission paths of a mesh network. The simulation results show that the proposed packet loss model can accurately estimate the packet loss in a mesh-based P2P network. Based on the proposed model, we also propose a peer selection mechanism which can effectively mitigate packet loss propagation by selecting at a parent-peer the candidate child-peers that can achieve the minimal packet loss probability compared to others, to transmit the FEC redundant substream.

In multiuser rate allocation in a wireless network, strategic users can bias the rate allocation by misrepresenting their bandwidth demands to a base station, leading to an unfair allocation. Game-theoretical approaches have been proposed to address the unfair allocation problems caused by the strategic users. However, existing approaches rely on a timeconsuming iterative negotiation process. Besides, they cannot completely prevent unfair allocations caused by inconsistent strategic behaviors. To address these problems, we propose a Search Based Pricing Mechanism to reduce the communication time and to capture a user's strategic behavior. Our simulation results show that the proposed method significantly reduce the communication time as well as converges stably to an optimal allocation.

Video transport over wireless networks requires retransmissions to successfully deliver video data to a receiver in case of packet loss, leading to increased delay time for the data to arrive at the receiver. Delay constraint is one of the most important requirements in real-time applications. A video packet arriving later than the presentation time will become useless for the client. In this paper, we propose a cross-layer content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs. Video packets of different importance are unequally protected with different retry limits at the media access control (MAC) layer. The error propagation effect of each packet is estimated to guide the determination of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Our scheme also analyzes the backoff time for each retry and then takes into account the estimated backoff time for retransmission scheduling. Experimental results show that the proposed adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, so as to improve video quality significantly.

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intraperson optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intra-person optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.

Due to the local smoothness characteristic of most real-world object surfaces, the per-pixel depth information can be efficiently compressed instead of using an additional color video channel. However, the complexity and hardware requirement are nearly two times higher than coding 2D video. By using sharing-of-motion-information method and analyzing the relationships between texture video and depth map sequence the complexity can be reduced. We propose predictive motion estimation algorithm to modify the texture motion information and mode reduction to reduce the complexity. In our algorithm, it doesn't need any additional effort on the decoder, and the image quality can also be approximated to original H.264 at low bit-rate. At high bit-rate, our algorithm outperforms other sharing of motion information algorithm. In the hardware design, due to complexity reduction we can reduce the hardware requirement. The overall depth map sequence encoder can be smaller than original H.264 encoder. We also solve the bubble cycles problem at 8times8 PE2D processing time.

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize expressional faces with one single training sample per class. In this paper, we modify the regularization-based optical flow algorithm by imposing constraints on some given point correspondences to compute precise pixel displacements and intensity variations. By using the optical flow computed for the input expression variant face with respect to a reference neutral face image, we remove the expression from the face image by elastic image warping to recognize the subject with facial expression. Experimental validation is given to show that the proposed expression normalization algorithm significantly improves the accuracy of face recognition on expression variant faces.

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation has been proposed in our pervious work. Facial expression normalization, from expressive to neutral facial images, based on optical flow analysis is discussed in this paper. In addition, we propose a new algorithm for nonnegative coefficient projection algorithm for projecting optical flow onto a facial expression subspace. Experimental validation is given to show that the proposed systems improve the accuracy of face and expression recognition on expressional face images.

The IEEE 802.16 standard (commonly known as WiMAX), which has been proposed as a new wireless broadband standard, is capable of delivering very high data rate and covering wide area. Video multicast service would become one potential application over WiMAX with the popularity of streaming applications in the Internet. Our method mainly uses adaptive modulation to achieve the goal of rate-adaptive multicast, and combines with the concept of layered multicast. According to the size of video layer, SS distribution, and available symbols, our method adaptively changes the modulations of each video layer in each group of picture (GOP) time. We also propose a Genetic Algorithm (GA) to reduce computational complexity when finding optimal modulation. Experimental results show that the proposed method can achieve promising performance.

In this paper, we present a two-pass intra-refresh transcoder for on-the-fly enhancing error resilience of a compressed video in a three-tier streaming system. Furthermore, we consider the problem of multicasting a video to multiple clients with diverse channel conditions. We propose a MINMAX loss rate estimation scheme to determine a single intra- refresh rate for all the clients in a multicast group. For the scenario that a quality variation constraint is imposed on the users, we also propose a grouping method to partition a multicast group of heterogeneous users into a minimal number of sub-groups to minimize the channel bandwidth consumption while meeting the quality variation constraint and achieving fairness among all sub-groups. Experimental results show that the proposed method can effectively mitigate the error propagation due to packet loss as well as achieve fairness not only among all sub-groups and also clients in a multicast group.

The performance of a face recognition system degrades incredibly due to the variation of facial appearance with different pose, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual face. The ideal solution is to reconstruct a 3D model from the input images and synthesize the virtual image with corresponding pose, which might be too complex to be implemented in a real-time application. By formulating this kind of solutions as a nonlinear pose normalization problem, we will propose an algorithm integrating the nonlinearity of kernel function and the efficiency of linear regression method, which modifies the linear assumption in local linear regression (LLR) method and makes the solution more resembling to the ideal one. Some discussions and experiments on CMU PIE database are carried out, and show that our proposed method performs well.

In this paper, we propose a content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs (WLANs). Video packets of different importance are unequally protected with different retry limits at the MAC layer. The loss impact of each packet is estimated to guide the selection of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Experimental results show that the proposed adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, thereby improving video quality significantly

In this paper, we present a two-pass error-resilience transcoding scheme based on content-aware intra-refresh (CAIR) for inserting error-resilience features to a compressed video. The proposed transcoder can adaptively vary the intra-refresh rate according to the video content and the channel's packet-loss rate to protect the most important macroblocks (MBs) against packet loss. Based on the CAIR transcoder, we propose a profit tracing scheme to improve the efficacy of intra-fresh allocation of the transcoder by avoiding wasting intra-refresh resource in MBs of high error-propagation ranks in a prediction path. Experimental results show that incorporating the proposed profit tracing scheme into CAIR scheme can achieve significant PSNR performance improvement over the CAIR scheme itself.

In this paper, we propose a content-aware retry limit adaptation scheme for video streaming over IEEE 802.11 wireless LANs (WLANs). Video packets of different importance are unequally protected with different retry limits at the MAC layer. The loss impact of each packet is estimated to guide the selection of its retry limit. More retry numbers are allocated to packets of higher loss impact to achieve unequal error protection. Our scheme also analyzes the backoff time for each retry and then takes into account the estimated backoff time for retransmission scheduling. Experimental results show that our adaptation scheme can effectively mitigate the error propagation due to packet loss and assure the on-time arrival of packets for presentation, thereby improving video quality significantly

In this paper, we propose a two-pass intra-refresh transcoding scheme for inserting error-resilience features to a compressed video at the media gateway of a three-tier streaming system. The proposed transcoder can adaptively vary the intra-refresh rate according to the video content and the channel’s packet-loss rate to protect the most important macroblocks (MBs) against packet loss. In this work, we consider the problem of multicast of video to multiple clients having disparate channel loss profiles. We propose a minmax loss-rate estimation scheme to select a single intra-refresh rate for all the clients. Experimental results show that the proposed method can effectively mitigate the error propagation due to packet loss, and its fairness for multicast.

Virtual avatars in many applications are constructed manually or by a single speech-driven model which needs a lot of training data and long training time. It’s an essential problem to build up a user-dependent model more efficiently. In this paper, a new adaptation method, called the partial linear regression (PLR), is proposed and adopted in an audio-driven talking head application. This method allows users to adapt the partial parameters from the available adaptive data while keeping the others unchanged. In our experiments, the PLR algorithm can retrench the hours of time spent on retraining a new user dependent model, and adjust the user-independent model to a more personalized one. The animated results with adapted models were 36% closer to the user-dependent model than using the pre-trained user-independent model.

Most of the facial animation applications, such as automatic face detection, recognition, and tracking are sensitive to the different lighting conditions and complicated background environment, especially for accurate expression analysis in virtual conferencing system. The main work in this research is to detect the faces under these complex situations and reduce the influence of different lighting. In addition, the pose estimation is the bottleneck of the whole framework, the speed of which is to be improved. An adaptive skin color model, scanning over the downsampled skin map, a modified mean shift algorithm and a new skin mask are used to refine and speed up the head tracking procedure. The lighting distribution is estimated by a second order function, and the lighting effect is compensated. Finally, we modify the good feature selection algorithm to the texture map to pick out the features, and then perform the analysis-by-synthesis pose estimation with the texture map of good features to speed up without losing accuracy.

A novel error resilience coding technique, named multiple description scalar quantization for fine granularity scalability (MDSQ-FGS), is presented to improve the temporal prediction efficiency of video coding over lossy packet networks and wireless channels. Despite easily adapting to channel bandwidth fluctuations, the coding efficiency (CE) of scalar coding techniques is low since only the base-layer is used in its motion prediction. But to achieve higher CE by using high quality reference frames in the enhancement-layer causes "drift" error propagation, caused by the mismatch between the reference frames used in encoding and decoding, when reference data are lost. The MDSQ-FGS video coding scheme is proposed to control drifting error without too much reduction in CE. Simulation results indicate that the proposed coder outperforms the normal MPEG-4 FGS coder for CE. It is also applicable for error prone wireless networks, for a mobile station moving between two access points.

In this paper, we propose efficient techniques and architectures for realizing spatial-downscaling transcoders in the DCT domain. We present efficient DCT-domain methods for arbitrary-size downscaling and upscaling. We show that, by integrating the downscaling process into the DCT-domain motion compensation (DCT-MC) operation for B-frames, the computation of DCT-MC and downscaling can be significantly reduced, leading to a simplified cascaded DCT-domain downscaling transcoder (CDDT) without introducing extra quality degradation. We also propose another scheme to further reduce the computation and storage cost which may introduce drifting errors. Experimental results show that the two proposed schemes can achieve significant computation reduction when compared with the original CDDT without any degradation or with introducing acceptable quality degradation, respectively.

Fast, reliable, and marker-free facial expression analysis still remains to be a difficult task in computer vision research. In this paper, the concept of speech-assisted facial expression analysis and synthesis is proposed, which shows that the speech-driven facial animation technique not only can be used for expression analysis. From the input speech, the mouth shape can is estimated from the audio-visual model. Thus, the large search space of mouth appearance is reduced for mouth tracking. Similarly, the modeling technique is extended from modeling speech and mouth shape to facial movements and detail facial texture changes. In this way, a virtual conferencing system with video realistic avatars is realized to meet real-time requirement.

This paper presents a region of interest transcoding scheme for multipoint video conferencing to enhance visual quality. In a multipoint video conference, usually there are only one or two active conferees at one time, which are the regions of interest to the other conferees involved. We propose a dynamic sub-window skipping scheme to firstly identify the active participants from the multiple incoming encoded video streams by calculating the motion activity of each sub-window and then dynamically reduce the frame rates of the motion inactive participants by skipping these less-important sub-windows. The bits saved from the skipping operation are reallocated to the active sub-windows to enhance the regions of interest. We also propose a low-complexity scheme to compose, as well as trace, the unavailable motion vectors with a good accuracy in the dropped inactive sub-windows after performing sub-window skipping. Simulation results show that the proposed methods not only significantly improve the visual quality of the active sub-windows without introducing serious visual quality degradation in the inactive ones, but also reduce the computational complexity and avoid whole-frame skipping. Moreover, the proposed algorithm is fully compatible with the H.263 video coding standard.

This paper describes the feasibility of selecting fractal feature vector based on M-band wavelet transform to classify ultrasonic liver images-normal liver, cirrhosis, and hepatoma. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Various classification algorithms based on respective texture measurements and filter banks are presented and tested. Classifications for the three sets of ultrasonic liver images reveal that the fractal feature vector based on M-band wavelet transform is trustworthy. A hierarchical classifier, which is based on the proposed feature extraction algorithm is at least 96.7% accurate in the distinction between normal and abnormal liver images and is at least 93.6% accurate in the distinction between cirrhosis and hepatoma liver images. Additionally, the criterion for feature selection is specified and employed for performance comparisons herein.

For molecular brain mapping research, a reference template is necessary for integration of experimental data from different laboratories. However, the concept of the conventional brain atlas cannot provide such a reference template that fulfills our requirements. We propose a specific algorithm for model averaging to construct a 3D reference template. It extracts the geometric information of all data sets by reconstructing the individual wireframe models, and extracts their skeletons for determining the average skeleton. Large-scale averaged models, pseudoaverage models, can be obtained by warping the individual models toward the average skeleton. The final average model can be yielded after a small-scale model averaging procedure that determines the geometric median of pseudoaverage models. In contrast with other algorithms, our algorithm allows more severe variations because of its two-level hierarchy.

Facial animation standardized by MPEG-4 provides a common form of description and transmission for talking head related applications. With animated talking heads, a virtual conferencing system can be created by providing a 3-D virtual environment for face-to-face communication and casual navigation. Not only bit-rate consumption is reduced, 3-D visualization is also provided which greatly improves the sense of presence when compared to conventional video conferencing system. An integrated architecture is presented by taking advantage of 2-D video coding and 3-D model-based coding to create video realistic avatars for virtual face-to-face conferencing system. Preliminary experimental results indicate more than 4 dB improvement in PSNR can be achieved at the same bit rate when compared to conventional video coding. The incorporation of 3-D facial models also enables rendering from arbitrary viewpoints for use in a virtual conferencing system.

A 3D textured polygonal facial model estimation algorithm is presented. The algorithm takes facial model estimation, texture mapping, and head pose estimation as complementary processes, which cooperate to adapt the facial model from a generic facial model to a user-accustomed one through image sequences. The proposed scheme is performed with a single camera without calibration and requires only a little manual adjustment, which proves to be a feasible approach for facial model estimation.

In model-based virtual conferencing systems, the changes of facial expressions on human faces are major focus of all users. In order to represent the detail changes of facial expressions, two algorithms for facial feature point tracking are developed to track motion of facial features. The first algorithm achieves medium to high accuracy with low computational complexity. And the second algorithm adopts hierarchical mesh models to algorithm is utilized to translate the tracking result to facial animation parameters, which can be used for driving MPEG-4 compliant talking heads.

This paper presents an H.323 standard compliant video conferencing system implementation. The proposed system not only serves as an MCU (Multipoint Control Unit) for multipoint connection but also provides a gateway function between the H.323 LAN (Local Area Network) and the H.324 WAN (Wide Area Network) users. The proposed video conferencing system provides user-friendly object compositing and manipulation features including 2-D video object scaling, re-positioning, rotating, and dynamic bit-allocation in a 3-D virtual environment. A segmentation scheme based on pre- stored background information is proposed for real-time segmentation of the foreground video objects at the client side. Chroma-key insertion is used to facilitate video objects extraction and manipulation. We have implemented the virtual conference system prototype with an integrated graphic user interface to demonstrate the feasibility of the proposed methods.

This paper presents a novel face-assisted video coding scheme to enhance the visual quality of the face regions in video telephony applications. A skin-color based face detection and tracking scheme is proposed to locate the face regions in real-time. After classifying the macroblocks into the face and non-face regions, we present a dynamic distortion weighting adjustment (DDWA) scheme to drop the static non-face macroblocks, and the saved bits are used to compensate the face region by adjusting the distortion weighting of the face macroblocks. The quality of face regions will thus be enhanced. Moreover, the computation originally required for the skipped macroblocks can also be saved. The experimental results show that the proposed method can significantly improve the PSNR and the subjective quality of face regions, while the degradation introduced on the non-face areas is relatively insensitive to human perception. The proposed algorithm is fully compatible with the H.263 standard, and the low complexity feature makes it well suited to implement for real-time applications.

This paper presents a dynamic rate control method for video transcoding to enhance the visual quality of the participants and regions of interest in multipoint video conferencing. This method firstly identifies the active conferees from the multiple incoming video streams by calculating the temporal and the spatial activities of the conferee sub-windows. The sub-windows of inactive participants are dropped and the saved bits are reallocated to the active sub-windows by using a rate-distortion optimized bit allocation approach. The simulation results show that the visual quality of the active sub-windows is significantly improved with the cost of degrading the temporal resolution of the inactive sub-windows which is relatively invisible to human perception. In addition, we also propose a dynamic distortion weighting adjustment based on H.263 TMN-8 framework to improve the quality of the regions of interest such as the face regions, since the face regions are usually the main focuses in video conferencing. The quality of face regions can be effectively enhanced at most frames in our simulation results. The computational complexity of the proposed algorithm is pretty low thus making it well suited for real-time applications.

In this paper, an implementation of a virtual chat room system for multimedia communications is presented. In addition to the conventional ability of chat room applications to transmit talk messages between users, the system provides a 3-D virtual environment and adopts 3-D facial models for character representation. Each participant can communicate with the others with keyboard, mouse, and his facial expressions. Essential implementation issues including the facial expression analysis process are detailed in this article.

This paper proposes a two-stage global motion estimation method and a hybrid coding algorithm for the model-based coding. In the first stage, global motion is estimated by the feature-based algorithm. The estimated result is further refined by the gradient method. The two-stage estimation algorithm is performed hierarchically to remove the influence of the local facial expression. In the estimation process, we also utilize the steerable pyramid to improve accuracy. The facial expression region is coded by the clip-and-paste method, and is predicted by a classified prototype coding technique. The prototype coding technique can greatly improve the coding efficiency for the facial expression. The areas, which are difficult to be described by generic models, are encoded by the proposed hierarchical motion segmentation algorithm. We segment the image to different moving areas which can be modeled by affine models in a split-and-merge manner. The segmented results with the estimated affine models can give good prediction for the unmodeled regions. Computer simulation results show that the proposed complete model-based coding scheme gives very good performance in terms of peak signal-to-noise ratio and compression ratio.

Conventional standard video coding schemes based on independent coding of nonoverlapping image blocks produce undesirable effects (blocking effect and color bleeding, etc.) at low bit rate. A hybrid coding scheme evolving from the standard MPEG codec that combines lapped orthogonal transform (LOT) and overlapped block motion compensation and quantization approaches based on human visual system sensitivity is proposed to remove the artifacts. The LOT is applied to compress the resulting prediction errors of the overlapped motion compensation. The LOT coefficients are quantized by a dynamic intra/inter scene adaptive quantizer that is designed according to the human visual system and scene analysis to optimize the decoded image quality. This technique involves automatically deriving the most favorable quantizer matrix from the motion and scene analysis of image sequences and scaling the resulting matrix further by considering the buffer status. If a scene change is detected from the motion information, the proposed adaptive quantization can also help solve the difficulty of the buffer controller and maintain the picture quality as constant as possible. Performance of the proposed hybrid coding scheme with scene adaptive quantizer is demonstrated by computer simulations using standard test sequences and compared to that of the conventional MPEG-2 coding schemes. Subjective and objective tests show that image quality is improved considerably and the required bit rate is much less than that of the conventional method. Particularly, the image quality can be kept almost consistent, and the undesirable artifacts are reduced considerably.

A new adaptive color controller for image scanning and printing processes is proposed. The objective of controlling colors is to achieve the desired accuracy of color reproduction under various viewing and graphic-arts conditions. Based on the plant inverse approach, the adaptive color controller is devised to eliminate color errors for image reproduction. And, it involves a novel feature-extraction method and a model-free estimator. In this approach, the feature-extraction method characterizes the extreme nonlinearity of any combination of color scanning and printing devices in terms of the human vision characteristics. For the consideration of easy implementation and fast image-processing speed, the higher-order CMAC neural network, which was proposed by Lane in 1991, is employed to work for the model-free approximation. In this paper, the colorimetric simulation illustrates the satisfactory performance. Finally, its effectiveness is also demonstrated by several colorimetric experiments.>

A fuzzy tracking system is developed for rotation-invariant image tracking. The conventional rotation-invariant methods cost too much time on transformation for invariant-feature extraction (e.g., circular harmonic). They am not suitable for the implementation of real-time operation. In this paper, a dual-template strategy is proposed. Only two parallel matched filters and a simple fuzzy logic are employed to construct the novel fuzzy tracking system. The experiments show that the system can track the target more accurately and more rapidly.>

The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images.

A model based estimation algorithm is presented for the analysis of left ventricle motion and deformation over a cardiac cycle. This model based approach allows the authors to decouple a nonlinear and complex estimation problem into simple and well structured sub-problems. They identify the global motion as the relative position and orientation change of the left ventricle as a whole and model its shape as a tapered ellipsoid. A recursive algorithm is developed by incorporating the modeling primitive into the estimation procedure. This recursive algorithm produces a good estimate of the global motion and left ventricle shape even when the given bifurcation points are unevenly distributed. Upon compensation for the global motion, a tensor analysis based approach is introduced to parameterize the localized deformation of left ventricle surface.>

An efficient algorithm for optimizing the maximum likelihood criterion of direction-of-arrival (DOA) problems is presented. The algorithm is based on two principal theorems: the first concerns the fast Gram-Schmidt orthogonalization of a Krylov subspace, and the second provides an alternating one-dimensional maximization procedure. A combiner-lattice filter structure that facilitates highly modular and concurrent VLSI implementation for the proposed algorithm has also been developed. By investigating this structure, an instructive physical insight of the ML criterion could be revealed as the operation of notch filtering. Simulation results that demonstrate the performance of the algorithm are included.>