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Series Introduction | p. iii |
Preface | p. v |
Pattern Recognition | p. 1 |
Introduction | p. 3 |
Patterns and Pattern Recognition | p. 3 |
Significance and Potential Function of the Pattern Recognition System | p. 5 |
Configuration of the Pattern Recognition System | p. 8 |
Representation of Patterns and Approaches to Their Machine Recognition | p. 16 |
Paradigm Applications | p. 23 |
Supervised and Unsupervised Learning in Pattern Recognition | p. 29 |
Nonparametric Decision Theoretic Classification | p. 33 |
Decision Surfaces and Discriminant Functions | p. 34 |
Linear Discriminant Functions | p. 38 |
Piecewise Linear Discriminant Functions | p. 42 |
Nonlinear Discriminant Functions | p. 49 |
[phi] Machines | p. 52 |
Potential Functions as Discriminant Functions | p. 57 |
Problems | p. 59 |
Nonparametric (Distribution-Free) Training of Discriminant Functions | p. 62 |
Weight Space | p. 62 |
Error Correction Training Procedures | p. 66 |
Gradient Techniques | p. 72 |
Training Procedures for the Committee Machine | p. 74 |
Practical Considerations Concerning Error Correction Training Methods | p. 76 |
Minimum-Squared-Error Procedures | p. 76 |
Problems | p. 79 |
Statistical Discriminant Functions | p. 82 |
Introduction | p. 82 |
Problem Formulation by Means of Statistical Design Theory | p. 83 |
Optimal Discriminant Functions for Normally Distributed Patterns | p. 93 |
Training for Statistical Discriminant Functions | p. 101 |
Application to a Large Data-Set Problem: A Practical Example | p. 102 |
Problems | p. 106 |
Clustering Analysis and Unsupervised Learning | p. 112 |
Introduction | p. 112 |
Clustering with an Unknown Number of Classes | p. 117 |
Clustering with a Known Number of Classes | p. 129 |
Evaluation of Clustering Results by Various Algorithms | p. 145 |
Graph Theoretical Methods | p. 146 |
Mixture Statistics and Unsupervised Learning | p. 161 |
Concluding Remarks | p. 164 |
Problems | p. 164 |
Dimensionality Reduction and Feature Selection | p. 168 |
Optimal Number of Features in Classification of Multivariate Gaussian Data | p. 168 |
Feature Ordering by Means of Clustering Transformation | p. 170 |
Canonical Analysis and Its Applications to Remote Sensing Problems | p. 172 |
Optimum Classification with Fisher's Discriminant | p. 182 |
Nonparametric Feature Selection Method Applicable to Mixed Features | p. 188 |
Problems | p. 190 |
Neural Networks for Pattern Recognition | p. 197 |
Multilayer Perceptron | p. 201 |
Some Preliminaries | p. 201 |
Pattern Mappings in a Multilayer Perceptron | p. 205 |
A Primitive Example | p. 219 |
Problems | p. 223 |
Radial Basis Function Networks | p. 225 |
Radial Basis Function Networks | p. 225 |
RBF Network Training | p. 231 |
Formulation of the Radial Basis Functions for Pattern Classification by Means of Statistical Decision Theory | p. 232 |
Comparison of RBF Networks with Multilayer Perceptrons | p. 234 |
Problems | p. 235 |
Hamming Net and Kohonen Self-Organizing Feature Map | p. 236 |
Hamming Net | p. 236 |
Kohonen Self-Organizing Feature Map | p. 246 |
Problems | p. 253 |
The Hopfield Model | p. 256 |
The Hopfield Model | p. 256 |
An Illustrative Example for the Explanation of the Hopfield Network Operation | p. 258 |
Operation of the Hopfield Network | p. 261 |
Problems | p. 267 |
Data Preprocessing for Pictorial Pattern Recognition | p. 269 |
Preprocessing in the Spatial Domain | p. 271 |
Deterministic Gray-Level Transformation | p. 271 |
Gray-Level Histogram Modification | p. 274 |
Smoothing and Noise Elimination | p. 298 |
Edge Sharpening | p. 303 |
Thinning | p. 333 |
Morphological Processing | p. 336 |
Boundary Detection and Contour Tracing | p. 343 |
Texture and Object Extraction from Textural Background | p. 352 |
Problems | p. 357 |
Pictorial Data Preprocessing and Shape Analysis | p. 363 |
Data Structure and Picture Representation by a Quadtree | p. 363 |
Dot-Pattern Processing with Voronoi Approach | p. 365 |
Encoding of a Planar Curve by Chain Code | p. 374 |
Polygonal Approximation of Curves | p. 374 |
Encoding of a Curve with B-Spline | p. 377 |
Shape Analysis via Medial Axis Transformation | p. 378 |
Shape Discrimination Using Fourier Descriptor | p. 380 |
Shape Description via the Use of Critical Points | p. 384 |
Shape Description via Concatenated Arcs | p. 385 |
Identification of Partially Obscured Objects | p. 392 |
Recognizing Partially Occluded Parts by the Concept of Saliency of a Boundary Segment | p. 394 |
Problems | p. 399 |
Transforms and Image Processing in the Transform Domain | p. 401 |
Formulation of the Image Transform | p. 403 |
Functional Properties of the Two-Dimensional Fourier Transform | p. 406 |
Sampling | p. 420 |
Fast Fourier Transform | p. 437 |
Other Image Transforms | p. 454 |
Enhancement by Transform Processing | p. 468 |
Problems | p. 476 |
Wavelets and Wavelet Transform | p. 481 |
Introduction | p. 481 |
Wavelets and Wavelet Transform | p. 484 |
Scaling Function and Wavelet | p. 486 |
Filters and Filter Banks | p. 490 |
Digital Implementation of DWT | p. 496 |
Applications | p. 509 |
Exemplary Applications | p. 511 |
Document Image Analysis | p. 513 |
Industrial Inspection | p. 529 |
Remote Sensing Applications | p. 545 |
Vision Used for Control | p. 551 |
Practical Concerns of Image Processing and Pattern Recognition | p. 561 |
Computer System Architectures for Image Processing and Pattern Recognition | p. 563 |
What We Expect to Achieve from the Point of View of Computer System Architecture | p. 563 |
Overview of Specific Logic Processing and Mathematical Computation | p. 564 |
Interconnection Networks for SIMD Computers | p. 566 |
Systolic Array Architecture | p. 566 |
Digitized Images | p. 573 |
Image Model and Discrete Mathematics | p. 579 |
Image Model | p. 579 |
Simplification of the Continuous Image Model | p. 581 |
Two-Dimensional Delta Function | p. 584 |
Additive Linear Operators | p. 586 |
Convolution | p. 587 |
Differential Operators | p. 590 |
Preliminaries of Some Methods Used Frequently in Image Preprocessing | p. 591 |
Problems | p. 595 |
Digital Image Fundamentals | p. 597 |
Sampling and Quantization of an Image | p. 597 |
Imaging Geometry | p. 599 |
Matrix Manipulation | p. 613 |
Definition | p. 613 |
Matrix Multiplication | p. 614 |
Partitioning of Matrices | p. 615 |
Computation of the Inverse Matrix | p. 617 |
Eigenvectors and Eigenvalues of an Operator | p. 621 |
Notation | p. 625 |
Bibliography | p. 645 |
Index | p. 691 |
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