19 June 2017 Artificial intelligence tools for pattern recognition
Author Affiliations +
Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044302 (2017); doi: 10.1117/12.2280310
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Elena Acevedo, Antonio Acevedo, Federico Felipe, Pedro Avilés, "Artificial intelligence tools for pattern recognition", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044302 (19 June 2017); doi: 10.1117/12.2280310; https://doi.org/10.1117/12.2280310

Content addressable memory

Genetic algorithms

Pattern recognition

Hough transforms

Artificial intelligence

Detection and tracking algorithms

Evolutionary algorithms

Back to Top