Illustrating essential aspects of adaptive image processing from a computational intelligence viewpoint, the second edition of Adaptive Image Processing: A Computational Intelligence Perspective provides an authoritative and detailed account of computational intelligence (CI) methods and algorithms for adaptive image processing in regularization, edge detection, and early vision.

With three new chapters and updated information throughout, the new edition of this popular reference includes substantial new material that focuses on applications of advanced CI techniques in image processing applications. It introduces new concepts and frameworks that demonstrate how neural networks, support vector machines, fuzzy logic, and evolutionary algorithms can be used to address new challenges in image processing, including low-level image processing, visual content analysis, feature extraction, and pattern recognition.

Emphasizing developments in state-of-the-art CI techniques, such as content-based image retrieval, this book continues to provide educators, students, researchers, engineers, and technical managers in visual information processing with the up-to-date understanding required to address contemporary challenges in image content processing and analysis.

Kim-Hui Yap, Ling Guan, Stuart William Perry, San Hau Wong

Introduction. Fundamentals of CI-Inspired Adaptive Image Restoration. Spatially Adaptive Image Restoration. Adaptive Regularization Using Evolutionary Computation. Blind Image Deconvolution. Edge Detection Using Model-Based Neural Networks. Image Analysis and Retrieval via Self-Organization. Genetic Optimization of Feature Representation for Compressed-Domain Image Categorization. Content-Based Image Retrieval Using Computational Intelligence Techniques.