Pattern Recognition: Ideas in Practice

Abstract

Methods and Machines.- 1 Setting the Scene.- 1.1. Sets.- 1.2. Motivation.- 1.3. Component Problems of Pattern Recognition.- 1.4. Relation between Pattern Recognition and Other Subjects.- 1.5. Lessons of This Book.- 2 A Review of Optical Pattern Recognition Techniques.- 2.1. Introduction.- 2.2. Nonholographic Optical Correlation.- 2.3. Holographic Cross-Correlation.- 2.4. Speech Recognition by Optical Correlation.- 2.5. Automatic Inspection.- 2.6. Normalized Cross-Correlation.- 2.7. Optoelectronic Transduction.- 2.8. Linear Discriminant Implementation.- 2.9. Numerical Discriminants.- 2.10. Transformations.- 2.11. Normalization and Segmentation.- 2.12. Feature Detection.- 2.13. Sequential Recognition Techniques.- 2.14. Interactive Pattern Recognition.- 2.15. Concluding Comment.- References.- 3 Pattern Recognition with Networks of Memory Elements.- 3.1. Introduction.- 3.2. The Random-Access Memory as a Learning Element.- 3.3. Combinational Networks.- 3.4. Sequential Learning Automata.- 3.5. The Computation of Some Global Properties.- 3.6. Other Forms of Feedback.- References.- 4 Classification and Data Analysis in Vector Spaces.- 4.1. Statement of the Problem.- 4.2. Classification Methods.- 4.3. Additional Data Analysis Techniques.- 4.4. Implementation in Hardware.- 4.5. Current Research.- 4.6. Appendix: Classifier Design Methods.- References.- 5 Decision Trees, Tables, and Lattices.- 5.1. Introduction.- 5.2. Feature Design for Decision Trees.- 5.3. The Design of Decision Trees.- 5.4. Decision Tables.- 5.5. Table Conversion Methods.- 5.6. Table-Splitting Methods.- 5.7. The Common Subtree Problem.- 5.8. Equivalence of Subtables.- 5.9. Table to Lattice Conversion.- 5.10. The Development of Decision Rules.- 5.11. Choosing the Set of Rules.- References.- 6 Parallel Processing Techniques.- 6.1. Introduction.- 6.2. Brief Survey of Proposed Parallel Processors.- 6.3. The CLIP Processors.- 6.4. Programming a CLIP Array.- 6.5. Processing Larger Image Areas.- 6.6. Future Trends.- References.- 7 Digital Image Processing.- 7.1. Introduction.- 7.2. Motivations for Image Processing.- 7.3. Image Processing Disciplines.- 7.4. Image Processing Equipment.- 7.5. Conclusions.- References.- 8 Cases in Scene Analysis.- 8.1. Introduction.- 8.2. Scope of Scene Analysis.- 8.3. Line Finding.- 8.4. Polyhedral Vision.- 8.5. Region Finding.- References.- Applications.- 9 The Control of a Printed Circuit Board Drilling Machine by Visual Feedback.- 9.1. Introduction.- 9.2. The Structure of a Practical Visually Controlled Machine.- 9.3. The Application of Visual Control to Drilling Printed Circuit Boards.- 9.4. Picture Processing Operators.- 9.5. Performance and Conclusions.- References.- 10 Industrial Sensory Devices.- 10.1. Introduction.- 10.2. Potential Areas of Application.- 10.3. Sensory Modalities.- 10.4. Applications.- 10.5. Constraints.- References.- 11 Image Analysis of Macro molecular Structures.- 11.1. Introduction.- 11.2. Two-Dimensional Image Filtering.- 11.3. Rotational Image Filtering.- 11.4. Image Simulation of Three-Dimensional Structures.- 11.5. Three-Dimensional Image Reconstruction.- References.- 12 Computer-Assisted Measurement in the Cytogenetic Laboratory.- 12.1. Introduction.- 12.2. The Laboratory Workload.- 12.3. Automatic Karyotyping.- 12.4. Classification.- 12.5. Counting and Finding Cells.- 12.6. Accurate Measurement.- References.- 13 Vehicle Sounds and Recognition.- 13.1. Introduction.- 13.2. Planning and Preprocessing.- 13.3. Feature Extraction.- 13.4. Moment Feature Space.- 13.5. Nonsinusoidal Transforms.- 13.5. Homomorphic Filtering.- 13.7. Conclusions.- References.- 14 Current Problems in Automatic Speech Recognition.- 14.1. Preliminaries.- 14.2. Isolated Word Recognizers.- 14.3. Machine Perception of Continuous Speech.- 14.4. Conclusions.- References.- 15 Pattern Recognition in Electroencephalography.- 15.1. Introduction and General Description of the Human Electroencephalogram.- 15.2. Clinical and Research Applications.- 15.3.