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Handbook of Computer Vision Algorithms in Image Algebra

By: Gerhard X. Ritter, Joseph N. Wilson

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Hardcover | 21 September 2000 | Edition Number 2

At a Glance

Hardcover
444 Pages

Edition Type
New edition

Dimensions(cm)
26.1 x 17.7 x 3.7

Hardcover

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$384.25

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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.

Industry 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

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Artificial Intelligence Computer Vision Image Processing Mathematics Algebra Graphical & Digital Media Applications Number Theory

Image Algebra	p. 1
Introduction	p. 1
Point Sets	p. 4
Value Sets	p. 10
Images	p. 13
Templates	p. 23
Recursive Templates	p. 33
Neighborhoods	p. 37
The p-Product	p. 42
Exercises	p. 47
References	p. 50
Image Enhancement Techniques	p. 55
Introduction	p. 55
Averaging of Multiple Images	p. 55
Local Averaging	p. 57
Variable Local Averaging	p. 57
Iterative Conditional Local Averaging	p. 58
Gaussian Smoothing	p. 59
Max-Min Sharpening Transform	p. 60
Smoothing Binary Images by Association	p. 62
Median Filter	p. 65
Unsharp Masking	p. 68
Local Area Contrast Enhancement	p. 70
Histogram Equalization	p. 71
Histogram Modification	p. 72
Lowpass Filtering	p. 73
Highpass Filtering	p. 81
Exercises	p. 82
References	p. 84
Edge Detection and Boundary Finding Techniques	p. 85
Introduction	p. 85
Binary Image Boundaries	p. 85
Edge Enhancement by Discrete Differencing	p. 87
Roberts Edge Detector	p. 90
Prewitt Edge Detector	p. 91
Sobel Edge Detector	p. 93
Wallis Logarithmic Edge Detection	p. 94
Frei-Chen Edge and Line Detection	p. 96
Kirsch Edge Detector	p. 99
Directional Edge Detection	p. 101
Product of the Difference of Averages	p. 103
Canny Edge Detection	p. 105
Crack Edge Detection	p. 109
Marr-Hildreth Edge Detection	p. 111
Local Edge Detection in Three-Dimensional Images	p. 114
Hierarchical Edge Detection	p. 116
Edge Detection Using K-Forms	p. 118
Hueckel Edge Operator	p. 122
Divide-and-Conquer Boundary Detection	p. 128
Edge Following as Dynamic Programming	p. 131
Exercises	p. 134
References	p. 135
Thresholding Techniques	p. 137
Introduction	p. 137
Global Thresholding	p. 137
Semithresholding	p. 138
Multilevel Thresholding	p. 140
Variable Thresholding	p. 141
Threshold Selection Using Mean and Standard Deviation	p. 141
Threshold Selection by Maximizing Between-Class Variance	p. 143
Threshold Selection Using a Simple Image Statistic	p. 149
Exercises	p. 153
References	p. 153
Thinning and Skeletonizing	p. 155
Introduction	p. 155
Pavlidis Thinning Algorithm	p. 155
Medial Axis Transform (MAT)	p. 157
Distance Transforms	p. 159
Zhang-Suen Skeletonizing	p. 163
Zhang-Suen Transform--Modified to Preserve Homotopy	p. 166
Thinning Edge Magnitude Images	p. 168
Exercises	p. 171
References	p. 171
Connected Component Algorithms	p. 173
Introduction	p. 173
Component Labeling for Binary Images	p. 173
Labeling Components with Sequential Labels	p. 176
Counting Connected Components by Shrinking	p. 178
Pruning of Connected Components	p. 181
Hole Filling	p. 182
Exercises	p. 183
References	p. 185
Morphological Transforms and Techniques	p. 187
Introduction	p. 187
Basic Morphological Operations: Boolean Dilations and Erosions	p. 187
Opening and Closing	p. 192
Salt and Pepper Noise Removal	p. 193
The Hit-and-Miss Transform	p. 195
Gray Value Dilations, Erosions, Openings, and Closings	p. 197
The Rolling Ball Algorithm	p. 199
Exercises	p. 201
References	p. 202
Linear Image Transforms	p. 205
Introduction	p. 205
Fourier Transform	p. 205
Centering the Fourier Transform	p. 208
Fast Fourier Transform	p. 211
Discrete Cosine Transform	p. 217
Walsh Transform	p. 221
The Haar Wavelet Transform	p. 225
Daubechies Wavelet Transforms	p. 233
Exercises	p. 239
References	p. 240
Pattern Matching and Shape Detection	p. 243
Introduction	p. 243
Pattern Matching Using Correlation	p. 243
Pattern Matching in the Frequency Domain	p. 247
Rotation Invariant Pattern Matching	p. 252
Rotation and Scale Invariant Pattern Matching	p. 255
Line Detection Using the Hough Transform	p. 257
Detecting Ellipses Using the Hough Transform	p. 264
Generalized Hough Algorithm for Shape Detection	p. 269
Exercises	p. 272
References	p. 273
Image Features and Descriptors	p. 275
Introduction	p. 275
Area and Perimeter	p. 275
Euler Number	p. 276
Chain Code Extraction and Correlation	p. 278
Region Adjacency	p. 283
Inclusion Relation	p. 286
Quadtree Extraction	p. 289
Position, Orientation, and Symmetry	p. 292
Region Description Using Moments	p. 294
Histogram	p. 296
Cumulative Histogram	p. 298
Texture Descriptors: Spatial Gray Level Dependence Statistics	p. 299
Exercises	p. 305
References	p. 306
Geometric Image Transformations	p. 309
Introduction	p. 309
Image Reflection and Magnification	p. 309
Nearest Neighbor Image Rotation	p. 311
Image Rotation using Bilinear Interpolation	p. 313
Application of Image Rotation to the Computation of Directional Edge Templates	p. 316
General Affine Transforms	p. 320
Fractal Constructs	p. 322
Iterated Function Systems	p. 327
Exercises	p. 329
References	p. 330
Neural Networks and Cellular Automata	p. 333
Introduction	p. 333
Hopfield Neural Network	p. 334
Bidirectional Associative Memory (BAM)	p. 340
Hamming Net	p. 345
Single-Layer Perceptron (SLP)	p. 349
Multilayer Perceptron (MLP)	p. 352
Cellular Automata and Life	p. 359
Solving Mazes Using Cellular Automata	p. 360
Exercises	p. 362
References	p. 364
The Image Algebra C++ Library	p. 367
Index	p. 413
Table of Contents provided by Syndetics. All Rights Reserved.

Handbook of Computer Vision Algorithms in Image Algebra

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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