Adaptive Image Processing: A Computational Intelligence Perspective, Second Edition, 2nd Edition (Hardback) book cover

Adaptive Image Processing

A Computational Intelligence Perspective, Second Edition, 2nd Edition

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

CRC Press

376 pages | 253 B/W Illus.

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Hardback: 9781420084351
pub: 2009-12-23
eBook (VitalSource) : 9781315218625
pub: 2018-10-03
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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.

Table of Contents


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


About the Series

Image Processing Series

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Software Development & Engineering / Systems Analysis & Design