Handbook of Computer Vision Algorithms in Image Algebra: 2nd Edition (Hardback) book cover

Handbook of Computer Vision Algorithms in Image Algebra

2nd Edition

By Joseph N. Wilson, Gerhard X. Ritter

CRC Press

448 pages

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pub: 2000-09-21
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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.

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

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.

Subject Categories

BISAC Subject Codes/Headings:
COM012000
COMPUTERS / Computer Graphics
MAT000000
MATHEMATICS / General
MAT022000
MATHEMATICS / Number Theory
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems