Artificial intelligence tools for pattern recognition

Elena Acevedo, Antonio Acevedo, Federico Felipe, Pedro Avilés

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationSecond International Workshop on Pattern Recognition
EditorsGuojian Chen, Xudong Jiang, Masayuki Arai
PublisherSPIE
ISBN (Electronic)9781510613508
DOIs
StatePublished - 2017
Event2nd International Workshop on Pattern Recognition, IWPR 2017 - Singapore, Singapore
Duration: 1 May 20173 May 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10443
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2nd International Workshop on Pattern Recognition, IWPR 2017
Country/TerritorySingapore
CitySingapore
Period1/05/173/05/17

Keywords

  • Artificial Intelligence
  • Associative memories
  • Feature selection
  • Genetic algorithm
  • Hough transform
  • Morphological operators
  • Pattern recognition
  • Tire prints

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