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A multi-population genetic algorithm for robust and fast ellipse detection

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Abstract

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a genetic algorithm with multiple populations (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse in the target image. The technique uses 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, using 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 in an image—and speed of computation.

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Acknowledgments

Any one who wishes to receive a copy of the image databases or the program can send an e-mail to kharma@ece.concordia.ca .

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Authors and Affiliations

  1. Electrical and Computer Engineering, Concordia University, 1455 Blvd. de Maisonneuve O, Montreal, QC, Canada, H3G1M8

    Jie Yao & Nawwaf Kharma

  2. Computer Science Departments, Concordia University, 1455 Blvd. de Maisonneuve O, Montreal, QC, Canada, H3G1M8

    Peter Grogono

Authors
  1. Jie Yao
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  2. Nawwaf Kharma
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  3. Peter Grogono
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Correspondence to Nawwaf Kharma.

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Yao, J., Kharma, N. & Grogono, P. A multi-population genetic algorithm for robust and fast ellipse detection. Pattern Anal Applic 8, 149–162 (2005). https://doi.org/10.1007/s10044-005-0252-7

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  • DOI: https://doi.org/10.1007/s10044-005-0252-7

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