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This book is aimed at final year and postgraduate students with some background in computer science, and researchers requiring a brief introduction to

either area: knowledge-based systems or image processing. The book is in three parts, parts 1 and 2 are designed to provide overviews of each of the areas (each part equating to a half semester course), part III giyes a report on current work in which the two converge before describing some of the issues involved in designing future knowledge-based image processing systems. Sample questions are also provided for parts 1 and 2 in an appendix (A).

This book attempts to give an insight into the two areas of research and current systems, and suggests a way forward for designing future systems.

Preface

Acknowledgements

Contributors

Part 1: Knowledge-Based Systems: An Overview

D. Graham and D. Martland

1 Introduction to Knowledge-Based Systems

1.1 Background to Knowledge-Based Systems

1.1.1 Search Strategies

1.2 What is a Knowledge-Based System?

1.2.1 Basic Architecture

1.2.2 Characteristics of an Expert System

1.2.3 Explaining Solutions

1.2.4 Uncertainty

1.2.5 Evaluating Expert Systems Generally

2 Knowledge-Based System Architectures and Applications

2.1 Knowledge-Based System Tools and Architectures

2.1.1 Expert System Shells

2.1.2 High-Level Programming Languages

2.1.3 Hybrid Systems

2.1.4 Skeletal Systems

2.1.5 Truth Maintenance Systems

2.1.6 Current Research Problems in Expert Systems

2.1.7 Professional Issues

2.2 Knowledge-Based System Applications

2.2.1 Early Systems

2.2.2 Current Systems

3 Knowledge Elicitation

3.1 Knowledge Acquisition and Knowledge Elicitation

3.2 Stages of Knowledge Acquisition

3.3 Knowledge Acquisition Methodologies

3.3.1 Machine-Aided Knowledge Acquisition

3.3.2 Human-Labour Oriented Approaches to Knowledge Elicitation

3.4 Knowledge Elicitation Techniques

3.5 Knowledge Elicitation Issues

3.6 Knowledge Acquisition Tools:TEIRESIAS and OPAL

4 Knowledge Representation

4.1 Schemes for Representing Knowledge

4.2 Issues in Knowledge Representation

4.3 Logical Representation Schemes

4.4 Procedural Representation Schemes:Production Systems

4.5 Network Representations: Semantic Networks

4.6 Structured Representations

4.6.1 Conceptual Dependency Theory

4.6.2 Frames

4.6.3 Scripts

4.6.4 Objects

5 Neural Networks

5.1 Introduction

5.2 Background to Neural Networks

5.2.1 Motivation

5.2.2 Biological Neurons

5.3 Feed-Forward Networks

5.4 Applying Neural Network Methods

5.5 Feed-Back Networks

5.5.1 Lateral Inhibition

5.5.2 Hopfietd Networks

5.6 Kohonen Networks

5.7 Software Support for Neural Networks

5.8 Hardware Support for Neural Networks

5.9 Image Processing Applications

5.10 Strategies for Developing Applications

Part 2: Image Processing: An Overview

A. Barrett

6 Introduction to Image Processing

6.1 Background

……

Part 3:Knowledge-Based Systems and Image Processing:D.Graham and A.Barrett

Appendix A:Exercises and Further Reading

References

Author Index

Subject Index

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