A genetic algorithm with sharing for the detection of 2D geometric primitives in images
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.
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.
KeywordsGenetic Algorithms Image Primitive extraction Sharing Hough Transform
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