An improved Hough transform voting scheme utilizing surround suppression

The Hough transform has been a frequently used method for detecting lines in images. However, when applying Hough transform and derived algorithms using the standard Hough voting scheme on real-world images, the methods often suffer considerable degeneration in performance, especially in detection rate, because of the large amount of edges given by complex background or texture. It is very likely that these edges form false peaks in Hough space and thus produce false positives in the final results, or even suppress true peaks and cause missing lines. To reduce the impact of these texture region edges, a novel method utilizing surround suppression is proposed in this paper. By introducing a measure of isotropic surround suppression, the new algorithm treats edge pixels differently, giving small weights to edges in texture regions and large weights to edges on strong and clear boundaries, and uses these weights to accumulate votes in Hough space. In this way, false peaks formed by texture region edges are suppressed, and the quality of detection results is improved. An efficient computation method for calculating the isotropic surround suppression was also given, accelerating the proposed algorithm. Experimental results on a real-world image base show that the new method improves line detection rate significantly, compared with the standard Hough transform and the Hough transform using gradient direction information to guide the voting process. Though slower than the other two methods, the new algorithm can be preferable in applications where detection rate is of the most concern and where there is no very strict requirement for high speed performance.