Advances in Information Systems Science

1 Pattern Recognition: Heuristics or Science?.- 1. Introduction.- 2. Principal Directions in Pattern Recognition.- 2.1. Basic Concepts.- 2.2. Heuristic Recognition Methods.- 2.3. Perceptrons.- 2.4. Learning As Approximation to a Decision Function.- 2.5. The Method of Stochastic Approximation.- 2.6. Methods Based on Assumptions About the Properties of the Observed Signals.- 2.7. Applied Results.- 3. Parametric Models of Signals.- 3.1. Distributions with Interfering Parameters.- 3.2. The Problem of Recognition of Complex Signals.- 3.3. The Statistical Problems of Supervised and Nonsupervised Learning.- 3.4. Parametric Models with Reference Patterns.- 4. The Method of Permissible Transformations.- 4.1. Formalization of the Concept of Resemblance.- 4.2. Permissible Transformations.- 4.3. Correlation Method.- 4.4. Effectiveness of the Correlation Method.- 5. Methods of Analyzing Complex Pictures.- 5.1. Formal Syntactic Rules for Constructing Complex Pictures.- 5.2. Description of Complex Pictures in the Presence of Noise (the Method of Reference Sequences).- 5.3. Examples of the Use of the Reference-Sequences Method.- 6. Conclusions.- References.- 2 Feature Compression.- 1. The Role of “Features” in Pattern Recognition.- 1.1. Four Kinds of Pattern Recognition and Features.- 1.2. Component and Composition—Structure Analysis.- 1.3. Pattern Recognition As Induction.- 1.4. Decision Procedure and Features.- 1.5. Selection of Variables.- 1.6. Distance and Feature.- 2. A Concrete Example of Feature Compression—Handwritten ZIP Code Reader.- 2.1. Nature of the Problem.- 2.2. Compression of Invariants.- 2.3. Local Features.- 2.4. Horizontal Zone Feature.- 2.5. Global Features.- 2.6. Feature Compression As Structural Analysis.- 3. Discriminatory Feature Compression—SELFIC.- 3.1. Rotations in Representation Space.- 3.2. Minimum-Entropy Principle.- 3.3. Basic Theorem of SELFIC.- 3.4. Discriminatory Feature Space and SELFIC.- 3.5. Object-Predicate Reciprocity.- 4. Characteristic Feature Compression—CLAFIC.- 4.1. Class-Feature Space.- 4.2. Subspace Model Versus Zone Model.- 4.3. Decision Procedures by Projection and by Entropy.- 5. Implications of Subspace Model—Fuzzy Class.- 5.1. Modular Nondistributive Predicate Lattice.- 5.2. Implications of the New Logic.- 5.3. Fuzzy Class.- References.- 3 Image Processing Principles and Techniques.- 1. Introduction.- 1.1. Central Problems.- 1.2. Processing for Data Compression.- 1.3. Processing for Enhancement.- 1.4. Processing for Classification.- 2. Filter Theory Applied to Images.- 2.1. Spatial Frequency Filtering.- 2.2. Matched Filtering.- 3. Statistical Decision Theory.- 3.1. Decision Theory Formalisms.- 3.2. Special Cases.- 3.3. Commentary on Applications.- 4. Adaptive Network Approaches.- 5. Image Features.- 5.1. Approximating Functions.- 5.2. Random Features.- 5.3. Feature Adaptation.- 5.4. Shape Features.- 5.5. Textural Features.- 5.6. Serially Derived Features.- 5.7. Picture Linguistics.- 5.8. Distance Features.- 6. Implementations: Staging.- 6.1. Realizable Decision Functions.- 6.2. Number of Stages.- 7. Implementations: Parallelism.- 7.1. All-Serial Methods.- 7.2. Parallel Operator, Serial Image Processing.- 7.3. Serial Operator, Parallel Image Processing.- 7.4. All-Parallel Methods.- 8. Electrooptical Devices.- 8.1. Point and Aperture Scanners.- 8.2. Image Parallel Devices.- 9. Digital Computers.- 9.1. The Fast Fourier Transform.- 9.2. Parallel Computers.- 10. Optical Techniques.- 10.1. Coherent Optics.- 10.2. Incoherent Optics.- 11. Comparison of Implementations.- 12. Conclusions.- References.- 4 Computer Graphics.- 1. Introduction.- 2. Devices for Computer Graphics.- 2.1. Noninteractive Graphic Output Devices.- 2.2. Noninteractive Graphic Input Devices.- 2.3. Input for Interaction.- 2.4. Interactive Display Operations.- 3. Modes of Interactive Graphic Systems.- 3.1. Shared Memory with Stand-Alone Dedicated Processor.- 3.2. Buffered Memory Systems.- 3.3. Large Machine with Satellite.- 3.4. Multiaccess Graphics.- 4. Data Structures.- 4.1. The Nature of Data Structure.- 4.2. List Structures.- 4.3. Ring and Associative Structures.- 4.4. Data Structure Operations.- 4.5. Choice of Data Structures.- 5. Graphics Software.- 5.1. Introduction.- 5.2. Techniques for Generation of Display File.- 5.3. Special Techniques.- 6. Graphic Languages.- 6.1. Introductory Remarks.- 6.2. Graphic Command Languages.- 6.3. Picture Processing Languages.- 7. Conclusions.- Appendix 1. Choice of Equations for Generating a Circle.- Appendix 2. Method Given by Forrest for Parametrizing a Conic.- References.- 5 Logical Design of Optimal Digital Networks by Integer Programming.- 1. Introduction.- 2. Features of Logical Design by Integer Programming.- 3. Design of an Optimal Combinational Network with a Given Type of Gate by Integer Programming.- 3.1. General Mathematical Formulation of Design Procedures with Threshold Gates.- 3.2. Design of an Optimal Network with NOR Gate or Other Types of Gates.- 4. Design of an Optimal Combinational Network with Building Blocks (or Composite Gates) by Integer Programming.- 4.1. Feed-Forward Network Formulation and Design Procedure of an Optimal Combinational Network.- 4.2. Computational Examples.- 4.3. Design of Optimal Networks with Composite Gates.- 5. Other Applications of the Integer Programming Logical Design Method.- 5.1. Design of Combinational Optimal Networks under Miscellaneous Conditions.- 5.2. Design of an Error-Correcting Optimal Network.- 5.3. Diagnosis of a Network by Integer Programming.- 5.4. Design of Optimal Sequential Networks by Integer Programming.- 6. Concluding Remarks.- References.