Abstract
Shape recognition is a challenging task when images contain overlapping, noisy, occluded, partial shapes. This paper addresses the task of matching input shapes with model shapes described in terms of features such as line segments and angles. The quality of matching is gauged using a measure derived from attributed shape grammars. We apply genetic algorithms to the partial shape-matching task. Preliminary results, using model shapes with 6 to 70 features each, are extremely encouraging.
Original language | English (US) |
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Pages (from-to) | 987-992 |
Number of pages | 6 |
Journal | Pattern Recognition Letters |
Volume | 18 |
Issue number | 10 |
DOIs | |
State | Published - Oct 1997 |
Externally published | Yes |
Keywords
- Attributed strings
- Genetic algorithms
- Partial shape matching
- Pattern recognition
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence