2nd Edition

Handbook of Computer Vision Algorithms in Image Algebra

By Joseph N. Wilson, Gerhard X. Ritter Copyright 2000

    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.

    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.

    Biography

    Joseph N. Wilson, Gerhard X. Ritter

    "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