A novel Hough transform based on eliminating particle swarm optimization and its applications |
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Authors: | H.D. Cheng [Author Vitae] Yanhui Guo [Author Vitae] Yingtao Zhang |
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Affiliation: | School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China |
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Abstract: | Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is employed to improve the speed of a HT. The parameters of the solution after Hough transformation are considered as the particle positions, and the EPSO algorithm searches the optimum solution by eliminating the “weakest” particles to speed up the computation. An accumulation array in Hough transformation is utilized as a fitness function of the EPSO algorithm. The experiments on numerous images show that the proposed approach can detect curves or contours of both noise-free and noisy images with much better performance. Especially, for noisy images, it can archive much better results than that obtained by using the existing HT algorithms. |
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Keywords: | Hough transform Particle swarm optimization (PSO) Eliminating particle swarm optimization (EPSO) Curve detection |
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