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.
Indeed, a 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 [18] 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:
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by using distance images instead of directly using contour images, which tends to smoothen the function to optimize,
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by using a GA-sharing technique, to detect several image primitives in the same step,
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by applying some recent theoretical results on GAs (about mutation probabilities) to reduce convergence time.
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© 1996 Springer-Verlag Berlin Heidelberg
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Lutton, E., Martinez, P. (1996). A genetic algorithm with sharing for the detection of 2D geometric primitives in images. In: Alliot, JM., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1995. Lecture Notes in Computer Science, vol 1063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61108-8_45
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DOI: https://doi.org/10.1007/3-540-61108-8_45
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