2nd Edition

Adaptive Image Processing A Computational Intelligence Perspective, Second Edition

    376 Pages 253 B/W Illustrations
    by CRC Press

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    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.

    Importance of Vision
    Adaptive Image Processing
    Three Main Image Feature Classes
    Difficulties in Adaptive Image-Processing System Design
    Computational Intelligence Techniques
    Scope of the Book
    Contributions of the Current Work
    Overview of This Book
    Fundamentals of CI-Inspired Adaptive Image Restoration
    Image Distortions
    Image Restoration
    Constrained Least Square Error
    Neural Network Restoration
    Neural Network Restoration Algorithms in the Literature
    An Improved Algorithm
    Implementation Considerations
    Numerical Study of the Algorithms
    Spatially Adaptive Image Restoration
    Dealing with Spatially Variant Distortion
    Adaptive Constraint Extension of the Penalty Function Model
    Correcting Spatially Variant Distortion Using Adaptive Constraints
    Semiblind Restoration Using Adaptive Constraints
    Implementation Considerations
    More Numerical Examples
    Numerical Examples
    Local Variance Extension of the Lagrange Model
    Regional Training Set Definition
    Determination of the Image Partition
    Edge-Texture Characterization Measure
    ETC Fuzzy HMBNN for Adaptive Regularization
    Theory of Fuzzy Sets
    Edge-Texture Fuzzy Model Based on ETC Measure
    Architecture of the Fuzzy HMBNN
    Estimation of the Desired Network Output
    Fuzzy Prediction of Desired Gray-Level Value
    Experimental Results
    Adaptive Regularization Using Evolutionary Computation
    Introduction to Evolutionary Computation
    ETC-pdf Image Model
    Adaptive Regularization Using Evolutionary Programming
    Experimental Results
    Other Evolutionary Approaches for Image Restoration
    Blind Image Deconvolution
    Computational Reinforced Learning
    Soft-Decision Method
    Simulation Examples
    Edge Detection Using Model-Based Neural Networks
    MBNN Model for Edge Characterization
    Network Architecture
    Training Stage
    Recognition Stage
    Experimental Results
    Image Analysis and Retrieval via Self-Organization
    Self-Organizing Map (SOM)
    Self-Organizing Tree Map (SOTM)
    SOTM in Impulse Noise Removal
    SOTM in Content-Based Retrieval
    Genetic Optimization of Feature Representation for Compressed-Domain Image Categorization
    Compressed-Domain Representation
    Problem Formulation
    Multiple-Classifier Approach
    Experimental Results
    Content-Based Image Retrieval Using Computational Intelligence Techniques
    Problem Description and Formulation
    Soft Relevance Feedback in CBIR
    Predictive-Label Fuzzy Support Vector Machine for Small Sample Problem


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