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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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Submitted on : Wednesday, May 24, 2006 - 3:46:16 PM
Last modification on : Thursday, February 3, 2022 - 11:14:48 AM
Long-term archiving on: : Tuesday, April 12, 2011 - 5:34:33 PM


  • HAL Id : inria-00074562, version 1



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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