Abdul H. Sadka

Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.

Compressed Video Communications

From the Publisher: The compression schemes applied for the storage and transmission of digital video data leave content sensitive to transmission errors, information loss and quality degradation. Recent developments in error resilience techniques allow improved quality of service of video communication over a range of network platforms. Digital video communications, supported by the Internet, ATM networks and Broadband ISDN, have undergone significant development over the past few years. Emerging applications include videoconferencing, tele-medicine and distance learning. This leading edge text addresses the problems associated with the delivery and design of video communication services. Presents a comprehensive overview of the principles and techniques employed in the improvement of the performance of video codecs in error prone environments Provides a performance evaluation and comparison of video coding standards, MPEG-4, H.261 and H.263 Outlines methods of video communication over mobile networks Provides guidance on quality enhancement and the meeting Quality of Service (QoS) requirements for digital video communications Accompanying CD-ROM containing video clips to illustrate the coding and error resilience technology described within the text A valuable resource for researchers and postgraduate students working with video communication technology, as well as practising electronic and communications engineers designing and implementing video communication systems and consultants working in the video, television, computing and communications industries.

Error-resilient video transcoding for robust internetwork communications using GPRS

A novel fully comprehensive mobile video communications system is proposed. The system exploits the useful rate management features of video transcoders and combines them with error resilience for the transmission of coded video streams over general packet radio service (GPRS) mobile-access networks. The error-resilient video transcoding operation takes place at a centralized point, referred to as a video proxy, which provides the necessary output transmission rates with the required amount of robustness. With the use of this proposed algorithm, error resilience can be added to an already compressed video stream at an intermediate stage at the edge of two or more different networks through two resilience schemes, namely the adaptive intra refresh (AIR) and feedback control signaling (FCS) methods. Both resilience tools impose an output rate increase which can also be prevented with the proposed novel technique. Thus, the presented scheme gives robust video outputs at near target transmission rates that only require the same number of GPRS timeslots as non-resilient schemes. Moreover, an ultimate robustness is also accomplished with the combination of the two resilience algorithms at the video proxy. Extensive computer simulations demonstrate the effectiveness of the proposed system.

Prioritisation of data partitioned MPEG-4 video over mobile networks

Despite much research in the field of mobile multimedia, delivery of real-time interactive video over noisy wireless channels remains a challenging problem. Two of the major issues in providing true end-to-end mobile multimedia capability are interoperability between platforms and networks and the poor performance of video compression algorithms in error-prone environments. This paper presents a method for prioritising data partitioned MPEG-4 video in a way suitable for transmission over a mobile network. The effectiveness of the technique is demonstrated by examining the performance when transmitted using the Real-Time Transport protocol over GPRS (General Packet Radio Service) data channels under varying channel conditions.

Combining Perceptual Features With Diffusion Distance for Face Recognition

Face recognition and identification is a very active research area nowadays due to its importance in both human computer and social interaction. Psychological studies suggest that face recognition by human beings can be featural, configurational, and holistic. In this paper, by incorporating spatially structured features into a histogram-based face-recognition framework, we intend to pursue consistent performance of face recognition. In our proposed approach, while diffusion distance is computed over a pair of human face images, the shape descriptions of these images are built using Gabor filters that consist of a number of scales and levels. It demonstrates that the use of perceptual features by Gabor filtering in combination with diffusion distance enables the system performance to be significantly improved, compared to several classical algorithms. The oriented Gabor filters lead to discriminative image representations that are then used to classify human faces in the database.

Automatic Single View-Based 3-D Face Synthesis for Unsupervised Multimedia Applications

Various 3-D face synthesis techniques have been proposed and extensively used in many applications. Compared with others, single view-based face synthesis technology allows unsupervised 3-D face reconstruction without any offline operations. Although many algorithms have been published, automatic and robust single view-based 3-D face synthesis still remains unsolved. In contrast to other methods, the single view-based 3-D face synthesis algorithm conducted in this paper enables automated 3-D face synthesis from an arbitrary head-and-shoulder image with the complex background. The developed system first detects the face using Bayesian skin-tone classification based on only the chrominance component, Cr. Based on the detected face, a few salient facial features, such as the corners of the eyebrows and contours of the eyes, mouth, and chin are in turn extracted using variant algorithms, including a dynamic chin extraction mechanism that will be detailed in this paper. Then, face model adaptation consisting of both global and local adaptations is imposed, according to geometric information provided by the extracted facial features. Finally, the 3-D specific face is synthesized using the adapted 3-D face model with a texture map directly derived from the input face image, followed by the implementation of facial animation using this synthesized face.

Feature extraction and clustering for dynamic video summarisation

In this paper an effective dynamic video summarisation algorithm is presented using audio-visual features extracted from videos. Audio, colour and motion features are dynamically fused using an adaptively weighting mechanism. Dissimilarities of temporal video segments are formulated using the extracted features before these segments are clustered using a fuzzy c-means algorithm with an optimally determined cluster number. The experimental results demonstrate the ability of the proposed algorithm to automatically summarise the videos with good performance.

Application of semantic features in face recognition

We propose a new face recognition strategy, which integrates the extraction of semantic features from faces with tensor subspace analysis. The semantic features consist of the eyes and mouth, plus the region outlined by the centers of the three components. A new objective function is generated to fuse the semantic and tensor models for finding similarity between a face and its counterpart in the database. Furthermore, singular value decomposition is used to solve the eigenvector problem in the tensor subspace analysis and to project the geometrical properties to the face manifold. Experimental results demonstrate that the proposed semantic feature-based face recognition algorithm has favorable performance with more accurate convergence and less computational efforts.

Adaptive fusion of particle filtering and spatio-temporal motion energy for human tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

Advances in Video Summarization and Skimming

This chapter summarizes recent advances in video abstraction for fast content browsing, skimming, transmission, and retrieval of massive video database which are demanded in many system applications, such as web multimedia, mobile multimedia, interactive TV, and emerging 3D TV. Video summarization and skimming aims to provide an abstract of a long video for shortening the navigation and browsing the original video. The challenge of video summarization is to effectively extract certain content of the video while preserving essential messages of the original video. In this chapter, the preliminary on video temporal structure analysis is introduced, various video summarization schemes, such as using low-level features, motion descriptors and Eigen-features, are described, and case studies on two practical summarization schemes are presented with experimental results.