A Genetic algorithm for the detection of 2D geometric primitives in images

Abstract : We investigate the use of genetic algorithms (GAs) in the framework of image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters. Indeeda GA is a stochastic technique, relatively slow, but which provides with an efficient tool to search in a high dimensional space. The philosophy of the method is very similar to the Hough transform, which is to search an optimum in a parameter space. However, we will see that the implementation is different. The idea of using a GA for that purpose is not new, Roth and Levine have proposed a method for 2D and 3D primitives in 1992. For the detection of 2D primitives, we re-implement that method and improve it mainly in three ways : by using distance images instead of directly using contour images, which tends to smoothen the function to optimize, by using a GA-sharing technique, to detect several image primitives in the same step, by applying some recent theoretical results on GAs (about mutation probabilities) to reduce convergence time.
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Rapport
[Research Report] RR-2110, INRIA. 1993
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https://hal.inria.fr/inria-00074562
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Soumis le : mercredi 24 mai 2006 - 15:46:16
Dernière modification le : mardi 17 avril 2018 - 11:28:47
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Evelyne Lutton, P. Martinez. A Genetic algorithm for the detection of 2D geometric primitives in images. [Research Report] RR-2110, INRIA. 1993. 〈inria-00074562〉

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