A novel Hough transform based on eliminating particle swarm optimization and its applications

Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and insensitiveness to noise, and its parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is studied to improve the speed of a Hough transform. The solutions of Hough transformation are considered as the particles 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 be used to 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.