Circle detection on images using genetic algorithms
Pattern Recognition Letters
Application of the Fisher-Rao Metric to Ellipse Detection
International Journal of Computer Vision
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Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Computers in Biology and Medicine
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Information Sciences: an International Journal
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Pattern Recognition
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This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a Multi-Population Genetic Algorithm (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse. It utilizes both evolution and clustering to direct the search for ellipses - full or partial. MPGA is explained in detail, and compared with both the widely used Randomized Hough Transform (RHT) and the Sharing Genetic Algorithm (SGA). In thorough and fair experimental tests, utilizing both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses, as well as speed of computation.