Title :
Shape recognition using genetic algorithms
Author :
Ozcan, Ender ; Mohan, Chilukuri K.
Author_Institution :
Sch. of Comput. & Inf. Sci., Syracuse Univ., NY, USA
Abstract :
Shape recognition is a challenging task when shapes overlap, forming noisy, occluded, partial shapes. The paper uses a genetic algorithm for matching input shapes with model shapes described in terms of features such as line segments and angles (extracted using traditional algorithms). The quality of matching is gauged using a measure derived from attributed shape grammars. Preliminary results, using shapes with about 30 features each, are extremely encouraging
Keywords :
attribute grammars; genetic algorithms; pattern matching; string matching; angles; attributed shape grammars; features; genetic algorithms; input shape matching; line segments; model shapes; noisy occluded partial shapes; overlapping shapes; shape recognition; Computational Intelligence Society; Data mining; Genetic algorithms; Impedance matching; Information science; Inspection; Noise shaping; Robots; Shape measurement; Very large scale integration;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542399
