| Contents | |
| 1. Introduction and Application Areas | chapter 0, chapter 1 |
| 2. Basics of Pattern Recognition (equivalence classes, position invariant feature extraction) |
chapter 2a 2b 2c |
| 3. Position Invariant Grayscale Image Detection (the CT class, parallel implementation, extension to 2-D case, effects of systematic and stochastic noise, clustering properties) |
chapter 3a 3b |
| 4. Position Invariant Contour Image
Detection (contour extraction, Fourier analysis, Fourier descriptors for the equivalence class of similarity and affine transformed patterns). |
chapter 4a 4b 4c 4d 4e | 5. General Approaches for Computing Invariants | chapter 5a 5b 5c |
| 6. Feature Reduction, Feature Selection | chapter 6 |
| 7. The Optimum Classifier, MAP- and MLE-criteria, Metrics | chapter 7a 7b 7c |
| 8. Neural Networks | chapter 8a 8b |
| 9. The Polynomial Classifier | chapter 9 |
| 10. Support Vector Machines | chapter 10 |