2nd Edition

Handbook of Computer Vision Algorithms in Image Algebra




ISBN 9780849300752
Published September 21, 2000 by CRC Press
448 Pages

USD $250.00

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Book Description

Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation of computer vision algorithms.

Updated to reflect recent developments and advances, the second edition continues to provide an outstanding introduction to image algebra. It describes more than 80 fundamental computer vision techniques and introduces the portable iaC++ library, which supports image algebra programming in the C++ language. Revisions to the first edition include a new chapter on geometric manipulation and spatial transformation, several additional algorithms, and the addition of exercises to each chapter.

The authors-both instrumental in the groundbreaking development of image algebra-introduce each technique with a brief discussion of its purpose and methodology, then provide its precise mathematical formulation. In addition to furnishing the simple yet powerful utility of image algebra, the Handbook of Computer Vision Algorithms in Image Algebra supplies the core of knowledge all computer vision practitioners need. It offers a more practical, less esoteric presentation than those found in research publications that will soon earn it a prime location on your reference shelf.

Table of Contents

IMAGE ALGEBRA
Point Sets
Value Sets
Images
Templates
Recursive Templates
Neighborhoods
The p-Product
IMAGE ENHANCEMENT TECHNIQUES
Averaging of Multiple Images
Local Averaging
Variable Local Averaging
Iterative Conditional Local Averaging
Gaussian Smoothing
Max-Min Sharpening Transform
Smoothing Binary Images by Association
Median Filter
Unsharp Masking
Local Area Contrast Enhancement
Histogram Equalization
Histogram Modification
Lowpass Filtering
Highpass Filtering
EDGE DETECTION AND BOUNDARY FINDING TECHNIQUES
Binary Image Boundaries
Edge Enhancement by Discrete Differencing
Roberts Edge Detector
Prewitt Edge Detector
Sobel Edge Detector
Wallis Logarithmic Edge Detection
Frei-Chen Edge and Line Detection
Kirsch Edge Detector
Directional Edge Detection
Product of the Difference of Averages
Canny Edge Detection
Crack Edge Detection
Local Edge Detection in Three-Dimensional Images
Hierarchical Edge Detection
Edge Detection Using K-Forms
Hueckel Edge Operator
Divide-and-Conquer Boundary Detection
Edge Following as Dynamic Programming
THRESHOLDING TECHNIQUES
Global Thresholding
Semithresholding
Multilevel Thresholding
Variable Thresholding
Threshold Selection Using Mean and Standard Deviation
Threshold Selection by Maximizing Between-Class Variance
Threshold Selection Using a Simple Image Statistic
THINNING AND SKELETONIZING
Pavlidis Thinning Algorithm
Medial Axis Transform (MAT)
Distance Transforms
Zhang-Suen Skeletonizing
Zhang-Suen Transform -- Modified to Preserve Homotopy
Thinning Edge Magnitude Images
CONNECTED COMPONENT ALGORITHMS
Component Labeling for Binary Images
Labeling Components with Sequential Labels
Counting Connected Components by Shrinking
Pruning of Connected Components
Hole Filling
MORHPHOLOGICAL TRANSFORMS AND TECHNIQUES
Basic Morphological Operations: Boolean Dilations and Erosions
Opening and Closing
Salt and Pepper Noise Removal
The Hit-and-Miss Transform
Gray Value Dilations, Erosions, Openings, and Closings
The Rolling Ball Algorithm
LINEAR IMAGE TRANSFORMS
Fourier Transform
Centering the Fourier Transform
Fast Fourier Transform
Discrete Cosine Transform
Walsh Transform
The Haar Wavelet Transform
Daubechies Wavelet Transforms
PATTERN MATCHING AND SHAPE DETECTION
Pattern Matching Using Correlation
Pattern Matching in the Frequency Domain
Rotation Invariant Pattern Matching
Rotation and Scale Invariant Pattern Matching
Line Detection Using the Hough Transform
Detecting Ellipses Using the Hough Transform
Generalized Hough Algorithm for Shape Detection
IMAGE FEATURES AND DESCRIPTORS
Area and Perimeter
Euler Number
Chain Code Extraction and Correlation
Region Adjacency
Inclusion Relation
Quadtree Extraction
Position, Orientation, and Symmetry
Region Description Using Moments
Histogram
Cumulative Histogram
Texture Descriptors
GEOMETRIC IMAGE TRANSFORMATIONS
Image Reflection and Magnification
Nearest Neighbor Image Rotation
Image Rotation using Bilinear Interpolation
Application of Image Rotation to the Computation of Directional Edge Templates
General Affine Transforms
Fractal Constructs
Iterated Function Systems
NEURAL NETWORKS AND CELLULAR AUTOMATA
Hopfield Neural Network
Bidirectional Associative Memory (BAM)
Hamming Net
Single-Layer Perceptron (SLP)
Multilayer Perceptron (MLP)
Cellular Automata and Life
Solving Mazes Using Cellular Automata
APPENDIX THE IMAGE ALGEBRA C++ LIBRARY
INDEX
NOTE: Each chapter also contains an Introduction and aReferences section. Chapters 2-12 also contain exercises.

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Reviews

"Every person who uses computer image processing...who is planning to use image processing...who is involved in the purchase of computer systems or software that involve image processing...should read this book. And if you are not in one of the above groups, you might want to read it anyway."
- Microscopy Research and Technique