Fast Robust GA-Based Ellipse Detection

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Fast Robust GA-Based Ellipse Detection
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. Keywords Genetic Algorithms, clustering, Sharing GA, Randomized Hough Transform, shape detection, ellipse detection.
Jie Yao, Nawwaf N. Kharma, Peter Grogono
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Jie Yao, Nawwaf N. Kharma, Peter Grogono
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