Pattern Analysis
Published on 1. July 1981
Book
Hardback
302 pages
978-3-540-10792-7 (ISBN)
Description
This book is devoted to pattern analysis, that is, the automatic construc tion of a symbolic description for a complex pattern, like an image or con nected speech. Pattern analysis thus tries to simulate certain capabilities which go without saying in any human central nervous system. The increasing interest and growing efforts at solving the problems related with pattern analysis are motivated by the challenge of the problem and the expected ap plications. Potential applications are numerous and result from the fact that data can be gathered and stored by modern devices in ever increasing extent, thus making the finding of particular interesting facts or events in these hosts of data an ever increasing problem. It was tried to organize the book around one particular view of pattern analysis: the view that pattern analysis requires an appropriate set of modules operating on a common data base which contains interme processing diate results of processing. Although other views are certainly possible, this one was adopted because the author feels that it is a useful idea, be cause the size of this book had to be kept within reasonable bounds, and because it facilitated the composition of fairly self-contained chapters.
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Heinrich Niemann
Pattern Analysis and Understanding
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02/1990
2nd Edition
Springer
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Content
1 Introduction.- 1.1 General Remarks.- 1.2 Definition of Concepts.- 1.3 Principal Approach.- 1.4 Scope of the Book.- 1.5 Applications.- 1.6 Related Fields.- 1.7 Summary.- 2 Preprocessing.- 2.1 Coding.- 2.1.1 Basic Approach.- 2.1.2 Pulse Code Modulation.- 2.1.3 Improvements of PCM.- 2.1.4 Transform Coding.- 2.1.5 Line Patterns.- 2.1.6 Additional Remarks.- 2.2 Normalization.- 2.2.1 Aim and Purpose.- 2.2.2 Size and Time.- 2.2.3 Intensity or Energy.- 2.2.4 Gray-Level Scaling.- 2.2.5 Geometric Corrections.- 2.2.6 Pseudocolors.- 2.3 Filtering.- 2.3.1 The Use of Filtering.- 2.3.2 Linear Systems.- 2.3.3 Homomorphic Systems.- 2.3.4 Filtering of Patterns.- 2.3.5 Nonlinear Methods.- 2.4 Linear Prediction.- 2.4.1 Predictor Coefficients.- 2.4.2 Spectral Modeling.- 2.5 Summary.- 3 Simple Constituents.- 3.1 Common Principles.- 3.2 Thresholding.- 3.2.1 Obtaining a Binary Image.- 3.2.2 Operations on Binary Images.- 3.3 Contours.- 3.3.1 Gray-Level Changes.- 3.3.2 Contour Filters.- 3.3.3 Line and Plane Segments.- 3.3.4 Statistical and Iterative Methods.- 3.3.5 Finding Straight and Curved Lines.- 3.3.6 Characterization of Contours.- 3.4 Regions.- 3.4.1 Homogeneity.- 3.4.2 Merging.- 3.4.3 Splitting.- 3.4.4 Split and Merge.- 3.4.5 Remarks.- 3.5 Texture.- 3.5.1 The Essence of Texture.- 3.5.2 Numerical Characterization.- 3.5.3 Syntactic Characterization.- 3.6 Image Sequences.- 3.7 Template Matching.- 3.7.1 Measuring Similarity.- 3.7.2 Hierarchical Matching.- 3.8 Segmentation of Speech.- 3.8.1 Measurements.- 3.8.2 Segmentation.- 3.9 Summary.- 4 Classification.- 4.1 Statistical Classification.- 4.2 Distribution-Free Classification.- 4.3 Nonparametric Classification.- 4.4 Learning.- 4.5 Additional Remarks.- 4.6 Summary.- 5 Data.- 5.1 Data Structures.- 5.1.1 Fundamental Structures.- 5.1.2 Advanced Structures.- 5.1.3 Remarks.- 5.2 Data Bases.- 5.2.1 A General Outline.- 5.2.2 Data Models.- 5.2.3 Data Sublanguages.- 5.3 Pattern Data.- 5.3.1 Data Structures for Patterns.- 5.3.2 Data Bases for Patterns.- 5.4 Summary.- 6 Control.- 6.1 The Problem.- 6.2 Interaction.- 6.3 Some Common Structures.- 6.4 Representation of Control.- 6.4.1 Abstract Programs.- 6.4.2 Hierarchical Graphs.- 6.4.3 Petri Nets.- 6.5 Control and Search Strategies.- 6.5.1 An Algorithm for State-Space Search.- 6.5.2 An Algorithm for Searching AND/OR Trees.- 6.5.3 Remarks on Pruning.- 6.5.4 Heuristic Strategies in Image Analysis.- 6.5.5 Heuristic Strategies in Speech Understanding.- 6.6 Summary.- 7 Knowledge Representation, Utilization, and Acquisition.- 7.1 Views of Knowledge.- 7.1.1 Levels and Hierarchies.- 7.1.2 Submodules.- 7.1.3 Aspects.- 7.1.4 Frames.- 7.2 Production Systems.- 7.2.1 General Properties.- 7.2.2 Applications in Pattern Analysis.- 7.3 Grammars.- 7.3.1 Introductory Remarks.- 7.3.2 String Grammars.- 7.3.3 High-Dimensional Grammars.- 7.3.4 Augmented Transition Networks.- 7.4 Graphs.- 7.4.1 Graph Grammars.- 7.4.2 Semantic Nets.- 7.4.3 Object Models.- 7.4.4 Dictionaries.- 7.5 Using Constraints.- 7.5.1 Two Examples.- 7.5.2 Relaxation Labeling.- 7.6 Acquisition of Knowledge (Learning).- 7.7 Summary.- 8 Systems for Pattern Analysis.- 8.1 Speech Understanding.- 8.2 Image Analysis.- 8.3 Summary.- 9 Things to Come.- References.