Shape recognition using genetic algorithms

Author

Ozcan, Ender ; Mohan, Chilukuri K.

Author_Institution

Sch. of Comput. & Inf. Sci., Syracuse Univ., NY, USA

fYear

1996

Firstpage

411

Lastpage

416

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1996., Proceedings of IEEE International Conference on

Conference_Location

Nagoya

Print_ISBN

0-7803-2902-3

Type

conf

DOI

10.1109/ICEC.1996.542399

Filename

542399

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1647768