Adaptive Image Processing: A Computational Intelligence Perspective, Second Edition

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Features

New to the Second Edition

  • 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
  • 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

Summary

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

Introduction
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
Analysis
Implementation Considerations
Numerical Study of the Algorithms
Summary
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
Summary
Acknowledgments
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
Summary
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
Summary
Blind Image Deconvolution
Computational Reinforced Learning
Soft-Decision Method
Simulation Examples
Conclusions
Edge Detection Using Model-Based Neural Networks
MBNN Model for Edge Characterization
Network Architecture
Training Stage
Recognition Stage
Experimental Results
Summary
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
Conclusion
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
Conclusion