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Proceedings Paper

Artificial intelligence tools for pattern recognition
Author(s): Elena Acevedo; Antonio Acevedo; Federico Felipe; Pedro Avilés
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Paper Abstract

In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044302 (19 June 2017); doi: 10.1117/12.2280310
Show Author Affiliations
Elena Acevedo, Instituto Politécnico Nacional (Mexico)
Antonio Acevedo, Instituto Politécnico Nacional (Mexico)
Federico Felipe, Instituto Politécnico Nacional (Mexico)
Pedro Avilés, Instituto Politécnico Nacional (Mexico)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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