Image Processing and Pattern Recognition Fundamentals and Techniques
by Shih, Frank Y.Edition: 1st
Format: Hardcover
Pub. Date: 2010-05-03
Publisher(s): Wiley-IEEE Press
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Summary
The techniques and applications in the area of image processing and pattern recognition have been growing tremendously. Containing the latest state-of-the-art techniques for image processing and pattern recognition, Image Processing and Pattern Recognition presents clear explanations of fundamentals and techniques as well as recently developed applications. Scores of graphs and examples, technical assistance, and practical tools illustrate principles of image processing and pattern recognition and help simplify the problems, allowing undergraduate and graduate students as well as professionals to easily grasp even complicated theories.
Author Biography
Frank Y. Shih is a professor jointly appointed in the Department of Computer Science, the Department of Electrical and Computer Engineering, and the Department of Biomedical Engineering at New Jersey Institute of Technology, Newark, New Jersey. He is also Director of Computer Vision Laboratory. He is an internationally renowned scholar and serves as the Editor-in-Chief for the International Journal of Multimedia Intelligence and Security (IJMIS). Dr. Shih is currently on the editorial boards of Pattern Recognition, Pattern Recognition Letters, the International Journal of Pattern Recognition and Artificial Intelligence, Journal of Information Hiding and Multimedia Signal Processing, Recent Patents on Engineering, Recent Patents on Computer Science, The Open Nanoscience Journal, the International Journal of Internet Protocol Technology, and the Journal of Internet Technology. The recipient of several awards for distinguished research, Dr. Shih has also made significant contributions to information hiding, focusing on the security and robustness of digital steganography and watermarking.
Table of Contents
PART I: FUNDAMENTALS.
1 INTRODUCTION.
1.1 The World of 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.
References.
2 MATHEMATICAL PRELIMINARIES.
2.1 Laplace Transform.
2.2 Fourier Transform.
2.3 Z-Transform.
2.4 Cosine Transform.
2.5 Wavelet Transform.
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.
4 MATHEMATICAL MORPHOLOGY.
4.1 Binary Morphology.
4.2 Opening and Closing.
4.3 Hit-or-Miss Transform.
4.4 Grayscale Morphology.
4.5 Basic Morphological Algorithms.
4.6 Morphological Filters.
5 IMAGE SEGMENTATION.
5.1 Thresholding.
5.2 Object (Component) Labeling.
5.3 Locating Object Contours by the Snake Model.
5.4 Edge Operators.
5.5 Edge Linking by Adaptive Mathematical Morphology.
5.6 Automatic Seeded Region Growing.
5.7 A Top-Down Region Dividing Approach.
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.
6.5 The 3D Euclidean Distance.
6.6 The Acquiring Approaches.
6.7 The Deriving Approaches.
6.8 The Shortest Path Planning.
6.9 Forward and Backward Chain Codes for Motion Planning.
6.10 A Few Examples.
7 IMAGE REPRESENTATION AND DESCRIPTION.
7.1 Run-Length Coding.
7.2 Binary Tree and Quadtree.
7.3 Contour Representation.
7.4 Skeletonization by Thinning.
7.5 Medial Axis Transformation.
7.6 Object Representation and Tolerance.
8 FEATURE EXTRACTION.
8.1 Fourier Descriptor and Moment Invariants.
8.2 Shape Number and Hierarchical Features.
8.3 Corner Detection.
8.4 Hough Transform.
8.5 Principal Component Analysis.
8.6 Linear Discriminate Analysis.
8.7 Feature Reduction in Input and Feature Spaces.
9 PATTERN RECOGNITION.
9.1 The Unsupervised Clustering Algorithm.
9.2 Bayes Classifier.
9.3 Support Vector Machine.
9.4 Neural Networks.
9.5 The Adaptive Resonance Theory Network.
9.6 Fuzzy Sets in Image Analysis.
PART II: APPLICATIONS.
10 FACE IMAGE PROCESSING AND ANALYSIS.
10.1 Face and Facial Feature Extraction.
10.2 Extraction of Head and Face Boundaries and Facial Features.
10.3 Recognizing Facial Action Units.
10.4 Facial Expression Recognition in JAFFE Database.
11 DOCUMENT IMAGE PROCESSING AND CLASSIFICATION.
11.1 Block Segmentation and Classification.
11.2 Rule-Based Character Recognition System.
11.3 Logo Identification.
11.4 Fuzzy Typographical Analysis for Character Preclassification.
11.5 Fuzzy Model for Character Classification.
12 IMAGE WATERMARKING.
12.1 Watermarking Classification.
12.2 Spatial Domain Watermarking.
12.3 Frequency-Domain Watermarking.
12.4 Fragile Watermark.
12.5 Robust Watermark.
12.6 Combinational Domain Digital Watermarking.
13 IMAGE STEGANOGRAPHY.
13.1 Types of Steganography.
13.2 Applications of Steganography.
13.3 Embedding Security and Imperceptibility.
13.4 Examples of Steganography Software.
13.5 Genetic Algorithm-Based Steganography.
14 SOLAR IMAGE PROCESSING AND ANALYSIS.
14.1 Automatic Extraction of Filaments.
14.2 Solar Flare Detection.
14.3 Solar Corona Mass Ejection Detection.
INDEX.
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