Adaptive Image Processing: A Computational Intelligence Perspective, Second Edition (Image Processing Series)

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. New to the Second Edition: A new chapter on a family of unsupervised algorithms with a basis in self-organization yet somewhat free from many of the constraints typical of other well-known self-organizing architectures New material on recent challenges in image content analysis and classification, including small sample problems and fuzzy user perception A new technique in visual query processing and visualization in 2D space New experiments and updates on perceptual error-based restoration 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.

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.

new To The Second Edition:

• a New Chapter On A Family Of Unsupervised Algorithms With A Basis In Self-organization Yet Somewhat Free From Many Of The Constraints Typical Of Other Well-known Self-organizing Architectures
• new Material On Recent Challenges In Image Content Analysis And Classification, Including Small Sample Problems And Fuzzy User Perception
• a New Technique In Visual Query Processing And Visualization In 2d Space
• new Experiments And Updates On Perceptual Error-based Restoration

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.

Contents 8 Preface 14 Chapter 1. Introduction 16 Chapter 2. Fundamentals of CI-Inspired Adaptive Image Restoration 40 Chapter 3. Spatially Adaptive Image Restoration 64 Chapter 4, Perceptually Motivated Image Restoration 104 Chapter 5. Model-Based Adaptive Image Restoration 138 Chapter 6. Adaptive Regularization Using Evolutionary Computation 184 Chapter 7. Blind Image Deconvolution 210 Chapter 8. Edge Detection Using Model-Based Neural Networks 254 Chapter 9. Image Analysis and Retrieval via Self-Organization 276 Chapter 10. Genetic Optimization of Feature Representation for Compressed-Domain Image Categorization 314 Chapter 11. Content-Based Image Retrieval Using Computational Intelligence Techniques 328 References 354 Index 368 Adaptive image processing is one of the most important techniques in visual information processing, especially in early vision such as image restoration, filtering, enhancement, and segmentation. This work focuses on applications of advanced CI techniques in image processing applications