Image Processing and Pattern Recognition Fundamentals and Techniques 1st Edition by Frank Shih ISBN 978-0470404614 0470404612
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ISBN 10: 0470404612
ISBN 13: 978-0470404614
Author: Frank Y. Shih
A comprehensive guide to the essential principles of image processing and pattern recognition
Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications.
Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic.
Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
Table of contents:
PART I
FUNDAMENTALS
1 INTRODUCTION
1.1 The World of Signals
1.1.1 One-Dimensional Signals
1.1.2 Two-Dimensional Signals
1.1.3 Three-Dimensional Signals
1.1.4 Multidimensional Signals
1.2 Digital Image Processing
1.3 Elements of an Image Processing System
Appendix 1.A Selected List of Books on Image Processing and Computer
Vision from Year 2000
1.A.1 Selected List of Books on Signal Processing from Year 2000
1.A.2 Selected List of Books on Pattern Recognition from Year 2000
References
2 MATHEMATICAL PRELIMINARIES
2.1 Laplace Transform
2.1.1 Properties of Laplace Transform
2.2 Fourier Transform
2.2.1 Basic Theorems
2.2.2 Discrete Fourier Transform
2.2.3 Fast Fourier Transform
2.3 Z-Transform
2.3.1 Definition of Z-Transform
2.3.2 Properties of Z-Transform
2.4 Cosine Transform
2.5 Wavelet Transform
References
3 IMAGE ENHANCEMENT
3.1 Grayscale Transformation
3.2 Piecewise Linear Transformation
3.3 Bit Plane Slicing
3.4 Histogram Equalization
3.5 Histogram Specification
3.6 Enhancement by Arithmetic Operations
3.7 Smoothing Filter
3.8 Sharpening Filter
3.9 Image Blur Types and Quality Measures
References
4 MATHEMATICAL MORPHOLOGY
4.1 Binary Morphology
4.1.1 Binary Dilation
4.1.2 Binary Erosion
4.2 Opening and Closing
4.3 Hit-or-Miss Transform
4.4 Grayscale Morphology
4.4.1 Grayscale Dilation and Erosion
4.4.2 Grayscale Dilation Erosion Duality Theorem
4.5 Basic Morphological Algorithms
4.5.1 Boundary Extraction
4.5.2 Region Filling
4.5.3 Extraction of Connected Components
4.5.4 Convex Hull
4.5.5 Thinning
4.5.6 Thickening
4.5.7 Skeletonization
4.5.8 Pruning
4.5.9 Morphological Edge Operator
4.5.9.1 The Simple Morphological Edge Operators
4.5.9.2 Blur-Minimum Morphological Edge Operator
4.6 Morphological Filters
4.6.1 Alternating Sequential Filters
4.6.2 Recursive Morphological Filters
4.6.3 Soft Morphological Filters
4.6.4 Order-Statistic Soft Morphological (OSSM) Filters
4.6.5 Recursive Soft Morphological Filters
4.6.6 Recursive Order-Statistic Soft Morphological Filters
4.6.7 Regulated Morphological Filters
4.6.8 Fuzzy Morphological Filters
References
5 IMAGE SEGMENTATION
5.1 Thresholding
5.2 Object (Component) Labeling
5.3 Locating Object Contours by the Snake Model
5.3.1 The Traditional Snake Model
5.3.2 The Improved Snake Model
5.3.3 The Gravitation External Force Field and The Greedy Algorithm
5.3.4 Experimental Results
5.4 Edge Operators
5.5 Edge Linking by Adaptive Mathematical Morphology
5.5.1 The Adaptive Mathematical Morphology
5.5.2 The Adaptive Morphological Edge-Linking Algorithm
5.5.3 Experimental Results
5.6 Automatic Seeded Region Growing
5.6.1 Overview of the Automatic Seeded Region Growing Algorithm
5.6.2 The Method for Automatic Seed Selection
5.6.3 The Segmentation Algorithm
5.6.4 Experimental Results and Discussions
5.7 A Top-Down Region Dividing Approach
5.7.1 Introduction
5.7.2 Overview of the TDRD-Based Image Segmentation
5.7.2.1 Problem Motivation
5.7.2.2 The TDRD-Based Image Segmentation
5.7.3 The Region Dividing and Subregion Examination Strategies
5.7.3.1 Region Dividing Procedure
5.7.3.2 Subregion Examination Strategy
5.7.4 Experimental Results
5.7.5 Potential Applications in Medical Image Analysis
5.7.5.1 Breast Boundary Segmentation
5.7.5.2 Lung Segmentation
References
6 DISTANCE TRANSFORMATION AND SHORTEST PATH PLANNING
6.1 General Concept
6.2 Distance Transformation by Mathematical Morphology
6.3 Approximation of Euclidean Distance
6.4 Decomposition of Distance Structuring Element
6.4.1 Decomposition of City-Block and Chessboard Distance Structuring Elements
6.4.2 Decomposition of the Euclidean Distance Structuring Element
6.4.2.1 Construction Procedure
6.4.2.2 Computational Complexity
6.5 The 3D Euclidean Distance
6.5.1 The 3D Volumetric Data Representation
6.5.2 Distance Functions in the 3D Domain
6.5.3 The 3D Neighborhood in the EDT
6.6 The Acquiring Approaches
6.6.1 Acquiring Approaches for City-Block and Chessboard Distance Transformations
6.6.2 Acquiring Approach for Euclidean Distance Transformation
6.7 The Deriving Approaches
6.7.1 The Fundamental Lemmas
6.7.2 The Two-Scan Algorithm for EDT
6.7.3 The Complexity of the Two-Scan Algorithm
6.8 The Shortest Path Planning
6.8.1 A Problematic Case of Using the Acquiring Approaches
6.8.2 Dynamically Rotational Mathematical Morphology
6.8.3 The Algorithm for Shortest Path Planning
6.9 Forward and Backward Chain Codes for Motion Planning
6.8.4 Some Examples
6.10 A Few Examples
References
7 IMAGE REPRESENTATION AND DESCRIPTION
7.1 Run-Length Coding
7.2 Binary Tree and Quadtree
7.3 Contour Representation
7.3.1 Chain Code and Crack Code
7.3.2 Difference Chain Code
7.3.3 Shape Signature
7.3.4 The Mid-Crack Code
7.4 Skeletonization by Thinning
7.4.1 The Iterative Thinning Algorithm
7.4.2 The Fully Parallel Thinning Algorithm
7.4.2.1 Definition of Safe Point
7.4.2.2 Safe Point Table
7.4.2.3 Deletability Conditions
7.4.2.4 The Fully Parallel Thinning Algorithm
7.4.2.5 Experimental Results and Discussion
7.5 Medial Axis Transformation
7.5.1 Thick Skeleton Generation
7.5.1.1 The Skeleton from Distance Function
7.5.1.2 Detection of Ridge Points
7.5.1.3 Trivial Uphill Generation
7.5.2 Basic Definitions
7.5.2.1 Base Point
7.5.2.2 Apex Point
7.5.2.3 Directional Uphill Generation
7.5.2.4 Directional Downhill Generation
7.5.3 The Skeletonization Algorithm and Connectivity Properties
7.5.4 A Modified Algorithm
7.6 Object Representation and Tolerance
7.6.1 Representation Framework: Formal Languages and Mathematical
Morphology
7.6.2 Dimensional Attributes
7.6.2.1 The 2D Attributes
7.6.2.2 The 3D Attributes
7.6.2.3 Tolerancing Expression
References
8 FEATURE EXTRACTION
8.1 Fourier Descriptor and Moment Invariants
8.2 Shape Number and Hierarchical Features
8.2.1 Shape Number
8.2.2 Significant Points Radius and Coordinates
8.2.3 Localization by Hierarchical Morphological Band-Pass Filter
8.3 Corner Detection
8.3.1 Asymmetrical Closing for Corner Detection
8.3.2 Regulated Morphology for Corner Detection
8.3.3 Experimental Results
8.4 Hough Transform
8.5 Principal Component Analysis
CONTENTS
8.6 Linear Discriminate Analysis
8.7 Feature Reduction in Input and Feature Spaces
8.7.1 Feature Reduction in the Input Space
8.7.2 Feature Reduction in the Feature Space
8.7.3 Combination of Input and Feature Spaces
References
9 PATTERN RECOGNITION
9.1 The Unsupervised Clustering Algorithm
9.1.1 Pass 1: Cluster’s Mean Vector Establishment
9.1.2 Pass 2: Pixel Classification
9.2 Bayes Classifier
9.3 Support Vector Machine
9.3.1 Linear Maximal Margin Classifier
9.3.2 Linear Soft Margin Classifier
9.3.3 Nonlinear Classifier
9.3.4 SVM Networks
9.4 Neural Networks
9.4.1 Programmable Logic Neural Networks
9.4.2 Pyramid Neural Network Structure
9.4.3 Binary Morphological Operations by Logic Modules
9.4.4 Multilayer Perceptron as Processing Modules
9.5 The Adaptive Resonance Theory Network
9.5.1 The ARTI Model and Learning Process
9.5.2 The ART2 Model
9.5.2.1 Learning in the ART2 Model
9.5.2.2 Functional-Link Net Preprocessor
9.5.3 Improvement of ART Model
9.5.3.1 Problem Analysis
9.5.3.2 An Improved ART Model for Pattern Classification
9.5.3.3 Experimental Results of the Improved Model
9.6 Fuzzy Sets in Image Analysis
9.6.1 Role of Fuzzy Geometry in Image Analysis
9.6.2 Definitions of Fuzzy Sets
9.6.3 Set Theoretic Operations
References
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Tags: Frank Shih, Image Processing, Pattern Recognition, Fundamentals and Techniques