Pattern Recognition and Image Preprocessing

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2002-01-11
  • Publisher: CRC Press

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Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection, novel computer system architectures, proven algorithms for solutions to common roadblocks in data processing, computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net, detailed appendices with data sets illustrating key concepts in the text, and more.

Author Biography

Sing-Tze Bow is Professor, Department of Electrical Engineering, Northern Illinois University, De Kalb.

Table of Contents

Series Introductionp. iii
Prefacep. v
Pattern Recognitionp. 1
Introductionp. 3
Patterns and Pattern Recognitionp. 3
Significance and Potential Function of the Pattern Recognition Systemp. 5
Configuration of the Pattern Recognition Systemp. 8
Representation of Patterns and Approaches to Their Machine Recognitionp. 16
Paradigm Applicationsp. 23
Supervised and Unsupervised Learning in Pattern Recognitionp. 29
Nonparametric Decision Theoretic Classificationp. 33
Decision Surfaces and Discriminant Functionsp. 34
Linear Discriminant Functionsp. 38
Piecewise Linear Discriminant Functionsp. 42
Nonlinear Discriminant Functionsp. 49
[phi] Machinesp. 52
Potential Functions as Discriminant Functionsp. 57
Problemsp. 59
Nonparametric (Distribution-Free) Training of Discriminant Functionsp. 62
Weight Spacep. 62
Error Correction Training Proceduresp. 66
Gradient Techniquesp. 72
Training Procedures for the Committee Machinep. 74
Practical Considerations Concerning Error Correction Training Methodsp. 76
Minimum-Squared-Error Proceduresp. 76
Problemsp. 79
Statistical Discriminant Functionsp. 82
Introductionp. 82
Problem Formulation by Means of Statistical Design Theoryp. 83
Optimal Discriminant Functions for Normally Distributed Patternsp. 93
Training for Statistical Discriminant Functionsp. 101
Application to a Large Data-Set Problem: A Practical Examplep. 102
Problemsp. 106
Clustering Analysis and Unsupervised Learningp. 112
Introductionp. 112
Clustering with an Unknown Number of Classesp. 117
Clustering with a Known Number of Classesp. 129
Evaluation of Clustering Results by Various Algorithmsp. 145
Graph Theoretical Methodsp. 146
Mixture Statistics and Unsupervised Learningp. 161
Concluding Remarksp. 164
Problemsp. 164
Dimensionality Reduction and Feature Selectionp. 168
Optimal Number of Features in Classification of Multivariate Gaussian Datap. 168
Feature Ordering by Means of Clustering Transformationp. 170
Canonical Analysis and Its Applications to Remote Sensing Problemsp. 172
Optimum Classification with Fisher's Discriminantp. 182
Nonparametric Feature Selection Method Applicable to Mixed Featuresp. 188
Problemsp. 190
Neural Networks for Pattern Recognitionp. 197
Multilayer Perceptronp. 201
Some Preliminariesp. 201
Pattern Mappings in a Multilayer Perceptronp. 205
A Primitive Examplep. 219
Problemsp. 223
Radial Basis Function Networksp. 225
Radial Basis Function Networksp. 225
RBF Network Trainingp. 231
Formulation of the Radial Basis Functions for Pattern Classification by Means of Statistical Decision Theoryp. 232
Comparison of RBF Networks with Multilayer Perceptronsp. 234
Problemsp. 235
Hamming Net and Kohonen Self-Organizing Feature Mapp. 236
Hamming Netp. 236
Kohonen Self-Organizing Feature Mapp. 246
Problemsp. 253
The Hopfield Modelp. 256
The Hopfield Modelp. 256
An Illustrative Example for the Explanation of the Hopfield Network Operationp. 258
Operation of the Hopfield Networkp. 261
Problemsp. 267
Data Preprocessing for Pictorial Pattern Recognitionp. 269
Preprocessing in the Spatial Domainp. 271
Deterministic Gray-Level Transformationp. 271
Gray-Level Histogram Modificationp. 274
Smoothing and Noise Eliminationp. 298
Edge Sharpeningp. 303
Thinningp. 333
Morphological Processingp. 336
Boundary Detection and Contour Tracingp. 343
Texture and Object Extraction from Textural Backgroundp. 352
Problemsp. 357
Pictorial Data Preprocessing and Shape Analysisp. 363
Data Structure and Picture Representation by a Quadtreep. 363
Dot-Pattern Processing with Voronoi Approachp. 365
Encoding of a Planar Curve by Chain Codep. 374
Polygonal Approximation of Curvesp. 374
Encoding of a Curve with B-Splinep. 377
Shape Analysis via Medial Axis Transformationp. 378
Shape Discrimination Using Fourier Descriptorp. 380
Shape Description via the Use of Critical Pointsp. 384
Shape Description via Concatenated Arcsp. 385
Identification of Partially Obscured Objectsp. 392
Recognizing Partially Occluded Parts by the Concept of Saliency of a Boundary Segmentp. 394
Problemsp. 399
Transforms and Image Processing in the Transform Domainp. 401
Formulation of the Image Transformp. 403
Functional Properties of the Two-Dimensional Fourier Transformp. 406
Samplingp. 420
Fast Fourier Transformp. 437
Other Image Transformsp. 454
Enhancement by Transform Processingp. 468
Problemsp. 476
Wavelets and Wavelet Transformp. 481
Introductionp. 481
Wavelets and Wavelet Transformp. 484
Scaling Function and Waveletp. 486
Filters and Filter Banksp. 490
Digital Implementation of DWTp. 496
Applicationsp. 509
Exemplary Applicationsp. 511
Document Image Analysisp. 513
Industrial Inspectionp. 529
Remote Sensing Applicationsp. 545
Vision Used for Controlp. 551
Practical Concerns of Image Processing and Pattern Recognitionp. 561
Computer System Architectures for Image Processing and Pattern Recognitionp. 563
What We Expect to Achieve from the Point of View of Computer System Architecturep. 563
Overview of Specific Logic Processing and Mathematical Computationp. 564
Interconnection Networks for SIMD Computersp. 566
Systolic Array Architecturep. 566
Digitized Imagesp. 573
Image Model and Discrete Mathematicsp. 579
Image Modelp. 579
Simplification of the Continuous Image Modelp. 581
Two-Dimensional Delta Functionp. 584
Additive Linear Operatorsp. 586
Convolutionp. 587
Differential Operatorsp. 590
Preliminaries of Some Methods Used Frequently in Image Preprocessingp. 591
Problemsp. 595
Digital Image Fundamentalsp. 597
Sampling and Quantization of an Imagep. 597
Imaging Geometryp. 599
Matrix Manipulationp. 613
Definitionp. 613
Matrix Multiplicationp. 614
Partitioning of Matricesp. 615
Computation of the Inverse Matrixp. 617
Eigenvectors and Eigenvalues of an Operatorp. 621
Notationp. 625
Bibliographyp. 645
Indexp. 691
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