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

We investigate the use of genetic algorithms (GAs) for 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 w...

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Bibliographic Details
Published inPattern Recognition, 1994 12th International Conference On. Vol. 1 Vol. 1; pp. 526 - 528 vol.1
Main Authors Lutton, E., Martinez, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Computational modeling
Genetic algorithms
Image edge detection
Image segmentation
Manufacturing
Optimization methods
Parameter estimation
Sampling methods
Simulated annealing
Stochastic processes
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ISBN0818662654
9780818662652
DOI10.1109/ICPR.1994.576345

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Summary:We investigate the use of genetic algorithms (GAs) for 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. 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.
ISBN:0818662654
9780818662652
DOI:10.1109/ICPR.1994.576345