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Chatterjee A., Siarry P. (eds.) Computational Intelligence in Image Processing

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Chatterjee A., Siarry P. (eds.) Computational Intelligence in Image Processing
Издательство Springer, 2013, -303 pp.
Computational intelligence-based techniques have firmly established themselves as viable, alternate, mathematical tools for more than a decade now. These techniques have been extensively employed in many systems and application domains, e.g., signal processing, automatic control, industrial and consumer electronics, robotics, finance, manufacturing systems, electric power systems, power electronics and drives, etc. Image processing is also an extremely potent area which has attracted the attention of many researchers interested in the development of new computational intelligence-based techniques and their suitable applications, in both research problems and in real-world problems. Initially, most of the attention and, hence, research efforts, were focused on developing conventional fuzzy systems, neural networks, and genetic algorithm-based solutions. But, as time elapsed, more sophisticated and complicated variations of these systems and newer branches of stochastic optimization algorithms have been proposed for providing solutions for a wide variety of image processing algorithms. As image processing essentially deals with multidimensional nonlinear mathematical problems, these computational intelligence-based techniques lend themselves perfectly to provide a solution platform for these problems. The interest in this area among researchers and developers is increasing day by day and this is visible in the form of huge volumes of research works that get published in leading international journals and in international conference proceedings.
When the idea of this book was first conceived, the goal was to mainly expose the readers to the cutting-edge research and applications that are going on across the domain of image processing where contemporary computational intelligence techniques can be and have been successfully employed. The result of the spirit behind this original idea and its fruitful implementation in terms of contributions from leading researchers across the globe, in varied related fields, is in front of you: a book containing 15 such chapters. A wide cross-section of image processing problems is covered within the purview of this book. They include problems in the domains of image enhancement, image segmentation, image analysis, image compression, image retrieval, image classification and clustering, image registration, etc.
The book focuses on the solution of these problems using state-of-the-art fuzzy systems, neuro-fuzzy systems, fractals, and stochastic optimization techniques. Among fuzzy systems and neuro-fuzzy systems, several chapters demonstrate how type-2 neuro-fuzzy systems, fuzzy transforms, fuzzy vector quantization, the concept of fuzzy entropy, etc., can be suitably utilized for solving these problems. Several chapters are also dedicated to the solution of image processing problems using contemporary stochastic optimization techniques. These include several modern bio- and nature-inspired global optimization algorithms like bacterial foraging optimization, biogeography-based optimization, genetic programming (GP), along with other popular stochastic optimization strategies, namely, multiobjective particle swarm optimization techniques and differential evolution algorithms. It is our sincere belief that this book will serve as a unified destination where interested readers will get detailed descriptions of many of these modern computational intelligence techniques and they will also obtain fairly good exposure to the modern image processing problem domains where such techniques can be successfully applied.
This book has been divided into four parts. Part I concentrates on discussion of several image preprocessing algorithms. Part II broadly covers image compression algorithms. Part III demonstrates how computational intelligence-based techniques can be effectively utilized for image analysis purposes, and Part IV elucidates how pattern recognition, classification, and clustering-based techniques can be developed for the purpose of image inferencing.
Part I Image Preprocessing Algorithms
Improved Digital Image Enhancement Filters Based on Type-2 Neuro-Fuzzy Techniques
Locally-Equalized Image Contrast Enhancement Using PSO-Tuned Sectorized Equalization
Hybrid BBO-DE Algorithms for Fuzzy Entropy-Based Thresholding
A Genetic Programming Approach for Image Segmentation
Part II Image Compression Algorithms
Fuzzy Clustering-Based Vector Quantization for Image Compression
Layers Image Compression and Reconstruction by Fuzzy Transforms
Modified Bacterial Foraging Optimization Technique for Vector Quantization-Based Image Compression
Part III Image Analysis Algorithms
A Fuzzy Condition-Sensitive Hierarchical Algorithm for Approximate Template Matching in Dynamic Image Sequence
Digital Watermarking Strings with Images Compressed by Fuzzy Relation Equations
Study on Human Brain Registration Process Using Mutual Information and Evolutionary Algorithms
Use of Stochastic Optimization Algorithms in Image Retrieval Problems
A Cluster-Based Boosting Strategy for Red Eye Removal
Part IV Image Inferencing Algorithms
Classifying Pathological Prostate Images by Fractal Analysis
Multiobjective PSO for Hyperspectral Image Clustering
A Computational Intelligence Approach to Emotion Recognition from the Lip-Contour of a Subject
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