Read more
The book gives the most total treatment of basic methods in pattern recognition including statistical, neurocomputing, syntactic/structural/grammatical approaches, feature selection and cluster analysis; and an extensive presentation of basic methods in computer vision including texture analysis and models, color, geometrical tools, image sequence analysis, etc. Major and unique applications are also covered, such as food handling using computer vision, non-destructive evaluation of materials, applications in economics and business, medical image recognition and understanding, etc. Broader system aspects are also examined, including optical pattern recognition and architectures for computer vision.
List of contents
Part 1 Basic methods in pattern recognition: statistical pattern recognition, K. Fukunaga; large-scale feature selection, J. Sklansky and W. Siedlecki. Part 2 Basic method in image processing and vision: vision engineering - designing computer vision systems, R. Chellapa and A. Rosenfeld; colour in computer vision, Q-T. Luong; model-based texture segmentation, R. Chellapa et al; positional estimation techniques for an autonomous mobile robot - a review, R. Talluri and J.K. Aggarwal. Part 3 Recognition applications: pattern recognition in geophysical signal processing and interpretation, Y. Li et al; optical handwritten Chinese character recognition, J.S. Huang. Part 4 Inspection and robotic applications: computer vision in food handling and sorting, H. Arnason and M. Asmundsson; quantitative 3-D methods in medical imaging, M. Loew. Part 5 Architectures and technology: optical pattern recognition for computer vision, D. Casasent; connectionist architectures in low-level image segmentation, W. Blanz and S. Gish.
