Syntactic and Structural Pattern Recognition
Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible features...
| Other Authors: | , , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1988, 1988
|
| Edition: | 1st ed. 1988 |
| Series: | NATO ASI Subseries F:, Computer and Systems Sciences
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- I. Matching and Parsing I
- A string correction method based on the context-dependent similarity
- An error-correcting parser for a context-free language based on the context-dependent similarity
- Ordered structural matching
- II. Matching and Parsing II
- A parsing algorithm for weighted grammars and substring recognition
- Computing the minimum error distance of graphs in 0 (n3) time with precedence graph grammars
- A unified view on tree metrics
- III. Applications I
- Problems in recognition of drawings
- Application of structural pattern recognition in histopathology
- Applications of multidimensional search to structural feature identification
- IV. Grammatical Inference and Clustering
- Learning from examples in sequences and grammatical inference
- An efficient algorithm for the inference of circuit-free automata
- Voronoi trees and clustering problems
- V. Image Understanding
- Hough-space decomposition for polyhedral scene analysis
- Running efficiently arc consistency
- Smith: an efficient model-based two dimensional shape matching technique
- Training and model generation for a syntactic curve network parser
- VI. Applications II
- Knowledge-based computer recognition of speech
- Computers viewing artists at work
- Face recognition from range data by structural analysis
- Cryptosystems for picture languages
- VII. Hybrid Approaches I
- Hybrid approaches
- An AI-structural approach to edge detection
- Building hierarchies-an algorithmic approach
- VIII. Hybrid Approaches II
- Combining logic based and syntactic techniques: a powerful approach
- A syntactic approach to planning
- IX. Working Sessions
- Working Group A: 2D and 3D Image Understanding
- Working Group B: Waveform and Speech Recognition
- Working Group C: Hybrid Techniques
- Working Group D: Models and Inference
- X. Panel
- Artificial Intelligence Versus Syntactic Techniques: Theoretical and Practical Issues
- XL List of Participants