Enhanced descriptions from Syndetics:

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 provided by Syndetics

• 1. Image Algebra (p. 1)
• 1.1. Introduction (p. 1)
• 1.2. Point Sets (p. 4)
• 1.3. Value Sets (p. 10)
• 1.4. Images (p. 13)
• 1.5. Templates (p. 23)
• 1.6. Recursive Templates (p. 33)
• 1.7. Neighborhoods (p. 37)
• 1.8. The p-Product (p. 42)
• 1.9. Exercises (p. 47)
• 1.10. References (p. 50)
• 2. Image Enhancement Techniques (p. 55)
• 2.1. Introduction (p. 55)
• 2.2. Averaging of Multiple Images (p. 55)
• 2.3. Local Averaging (p. 57)
• 2.4. Variable Local Averaging (p. 57)
• 2.5. Iterative Conditional Local Averaging (p. 58)
• 2.6. Gaussian Smoothing (p. 59)
• 2.7. Max-Min Sharpening Transform (p. 60)
• 2.8. Smoothing Binary Images by Association (p. 62)
• 2.9. Median Filter (p. 65)
• 2.10. Unsharp Masking (p. 68)
• 2.11. Local Area Contrast Enhancement (p. 70)
• 2.12. Histogram Equalization (p. 71)
• 2.13. Histogram Modification (p. 72)
• 2.14. Lowpass Filtering (p. 73)
• 2.15. Highpass Filtering (p. 81)
• 2.16. Exercises (p. 82)
• 2.17. References (p. 84)
• 3. Edge Detection and Boundary Finding Techniques (p. 85)
• 3.1. Introduction (p. 85)
• 3.2. Binary Image Boundaries (p. 85)
• 3.3. Edge Enhancement by Discrete Differencing (p. 87)
• 3.4. Roberts Edge Detector (p. 90)
• 3.5. Prewitt Edge Detector (p. 91)
• 3.6. Sobel Edge Detector (p. 93)
• 3.7. Wallis Logarithmic Edge Detection (p. 94)
• 3.8. Frei-Chen Edge and Line Detection (p. 96)
• 3.9. Kirsch Edge Detector (p. 99)
• 3.10. Directional Edge Detection (p. 101)
• 3.11. Product of the Difference of Averages (p. 103)
• 3.12. Canny Edge Detection (p. 105)
• 3.13. Crack Edge Detection (p. 109)
• 3.14. Marr-Hildreth Edge Detection (p. 111)
• 3.15. Local Edge Detection in Three-Dimensional Images (p. 114)
• 3.16. Hierarchical Edge Detection (p. 116)
• 3.17. Edge Detection Using K-Forms (p. 118)
• 3.18. Hueckel Edge Operator (p. 122)
• 3.19. Divide-and-Conquer Boundary Detection (p. 128)
• 3.20. Edge Following as Dynamic Programming (p. 131)
• 3.21. Exercises (p. 134)
• 3.22. References (p. 135)
• 4. Thresholding Techniques (p. 137)
• 4.1. Introduction (p. 137)
• 4.2. Global Thresholding (p. 137)
• 4.3. Semithresholding (p. 138)
• 4.4. Multilevel Thresholding (p. 140)
• 4.5. Variable Thresholding (p. 141)
• 4.6. Threshold Selection Using Mean and Standard Deviation (p. 141)
• 4.7. Threshold Selection by Maximizing Between-Class Variance (p. 143)
• 4.8. Threshold Selection Using a Simple Image Statistic (p. 149)
• 4.9. Exercises (p. 153)
• 4.10. References (p. 153)
• 5. Thinning and Skeletonizing (p. 155)
• 5.1. Introduction (p. 155)
• 5.2. Pavlidis Thinning Algorithm (p. 155)
• 5.3. Medial Axis Transform (MAT) (p. 157)
• 5.4. Distance Transforms (p. 159)
• 5.5. Zhang-Suen Skeletonizing (p. 163)
• 5.6. Zhang-Suen Transform--Modified to Preserve Homotopy (p. 166)
• 5.7. Thinning Edge Magnitude Images (p. 168)
• 5.8. Exercises (p. 171)
• 5.9. References (p. 171)
• 6. Connected Component Algorithms (p. 173)
• 6.1. Introduction (p. 173)
• 6.2. Component Labeling for Binary Images (p. 173)
• 6.3. Labeling Components with Sequential Labels (p. 176)
• 6.4. Counting Connected Components by Shrinking (p. 178)
• 6.5. Pruning of Connected Components (p. 181)
• 6.6. Hole Filling (p. 182)
• 6.7. Exercises (p. 183)
• 6.8. References (p. 185)
• 7. Morphological Transforms and Techniques (p. 187)
• 7.1. Introduction (p. 187)
• 7.2. Basic Morphological Operations: Boolean Dilations and Erosions (p. 187)
• 7.3. Opening and Closing (p. 192)
• 7.4. Salt and Pepper Noise Removal (p. 193)
• 7.5. The Hit-and-Miss Transform (p. 195)
• 7.6. Gray Value Dilations, Erosions, Openings, and Closings (p. 197)
• 7.7. The Rolling Ball Algorithm (p. 199)
• 7.8. Exercises (p. 201)
• 7.9. References (p. 202)
• 8. Linear Image Transforms (p. 205)
• 8.1. Introduction (p. 205)
• 8.2. Fourier Transform (p. 205)
• 8.3. Centering the Fourier Transform (p. 208)
• 8.4. Fast Fourier Transform (p. 211)
• 8.5. Discrete Cosine Transform (p. 217)
• 8.6. Walsh Transform (p. 221)
• 8.7. The Haar Wavelet Transform (p. 225)
• 8.8. Daubechies Wavelet Transforms (p. 233)
• 8.9. Exercises (p. 239)
• 8.10. References (p. 240)
• 9. Pattern Matching and Shape Detection (p. 243)
• 9.1. Introduction (p. 243)
• 9.2. Pattern Matching Using Correlation (p. 243)
• 9.3. Pattern Matching in the Frequency Domain (p. 247)
• 9.4. Rotation Invariant Pattern Matching (p. 252)
• 9.5. Rotation and Scale Invariant Pattern Matching (p. 255)
• 9.6. Line Detection Using the Hough Transform (p. 257)
• 9.7. Detecting Ellipses Using the Hough Transform (p. 264)
• 9.8. Generalized Hough Algorithm for Shape Detection (p. 269)
• 9.9. Exercises (p. 272)
• 9.10. References (p. 273)
• 10. Image Features and Descriptors (p. 275)
• 10.1. Introduction (p. 275)
• 10.2. Area and Perimeter (p. 275)
• 10.3. Euler Number (p. 276)
• 10.4. Chain Code Extraction and Correlation (p. 278)
• 10.5. Region Adjacency (p. 283)
• 10.6. Inclusion Relation (p. 286)
• 10.7. Quadtree Extraction (p. 289)
• 10.8. Position, Orientation, and Symmetry (p. 292)
• 10.9. Region Description Using Moments (p. 294)
• 10.10. Histogram (p. 296)
• 10.11. Cumulative Histogram (p. 298)
• 10.12. Texture Descriptors: Spatial Gray Level Dependence Statistics (p. 299)
• 10.13. Exercises (p. 305)
• 10.14. References (p. 306)
• 11. Geometric Image Transformations (p. 309)
• 11.1. Introduction (p. 309)
• 11.2. Image Reflection and Magnification (p. 309)
• 11.3. Nearest Neighbor Image Rotation (p. 311)
• 11.4. Image Rotation using Bilinear Interpolation (p. 313)
• 11.5. Application of Image Rotation to the Computation of Directional Edge Templates (p. 316)
• 11.6. General Affine Transforms (p. 320)
• 11.7. Fractal Constructs (p. 322)
• 11.8. Iterated Function Systems (p. 327)
• 11.9. Exercises (p. 329)
• 11.10. References (p. 330)
• 12. Neural Networks and Cellular Automata (p. 333)
• 12.1. Introduction (p. 333)
• 12.2. Hopfield Neural Network (p. 334)
• 12.3. Bidirectional Associative Memory (BAM) (p. 340)
• 12.4. Hamming Net (p. 345)
• 12.5. Single-Layer Perceptron (SLP) (p. 349)
• 12.6. Multilayer Perceptron (MLP) (p. 352)
• 12.7. Cellular Automata and Life (p. 359)
• 12.8. Solving Mazes Using Cellular Automata (p. 360)
• 12.9. Exercises (p. 362)
• 12.10. References (p. 364)
• Appendix The Image Algebra C++ Library (p. 367)
• Index (p. 413)