| 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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