Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Development of Fast Motion Estimation Algorithms for Video Comression

With the increasing popularity of technologies such as Internet streaming video and video conferencing, video compression has became an essential component of broadcast and entertainment media. Motion Estimation (ME) and compensation techniques, which can eliminate temporal redundancy between adjacent frames effectively, have been widely applied to popular video compression coding standards such as MPEG-2, MPEG-4. Traditional fast block matching algorithms are easily trapped into the local minima resulting in degradation on video quality to some extent after decoding. Since Evolutionary Computing Techniques are suitable for achieving global optimal solution, these techniques are introduced to do Motion Estimation procedure in this thesis. Zero Motion prejudgement is also included which aims at finding static macroblocks (MB) which do not need to perform remaining search thus reduces the computational cost. Simulation results obtained show that the proposed Clonal Particle Swarm Optimization algorithm given a very good improvement in reducing the computations overhead and achieves very good Peak Signal to Noise Ratio (PSNR) values, which makes the techniques more efficient than the conventional searching algorithms. To reduce the Motion vector overhead in Bidirectional frame prediction, in this thesis novel Bidirectional Motion Estimation algorithm based on PSO is also proposed and results shows that the proposed method can significantly reduces the computational complexity involved in the Bidirectional frame prediction and also least prediction error in all video sequence

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Fractal block coding techniques in image compression

Fractal block coding is a relatively new scheme for image compression. In this dissertation, several ádvanced schemes are proposed based upon Jacquin’s fractal block coding scheme. Exploiting self-similarity at different target block size levels is proposed which allows the self-similarity in the image to be exploited further. Smoother areas are coded with bigger target block sizes while fíne details are coded with smaller target block sizes. More image parts coded at a higher coding level will result in a lower bit rate. Removal of affine-block-wise self-similarity is proposed which includes block-wise self-similarity as a special case. With the utilisation of affineblock-wise self-similarity, the library is substantially enriched which results in a higher probability of coding a target block at a higher coding level. A very fast multi-level fractal block coding scheme exploiting affine-block-wise selfsimilarities is proposed. In the fast coding scheme, self-similarity in the very local area of the target block to be coded is exploited. By using affine-block-wise self-similarity, local correlations are exploited to a much further extent. The number of library blocks used for coding a target block is substantially reduced which results in very fast coding scheme. The proposed fast coding scheme outperforms previous implementations of the fractal block coding technique. A hybrid fractal block coding and DCT scheme is proposed which codes a subsampled image using fractal block coding techniques. The fractal codes are used to decode by zooming to the original image size. The DCT technique is introduced to code the residue image. The proposed scheme is better than the pure fractal block coding scheme. The advanced fractal block coding schemes and the hybrid coder for still images are also applied to video compression which also give some promising simulation results

Surface Reconstruction and Evolution from Multiple Views

Applications like 3D Telepresence necessitate faithful 3D surface reconstruction of the object and 3D data compression in both spatial and temporal domains. This makes us feel immersed in virtual environments there by making 3D Telepresence a powerful tool in many applications. Hence 3D surface reconstruction and 3D compression are two challenging problems which are addressed in this thesis

Measurements, Models, Systems and Design

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