An improved sampling strategy for randomized hough transform based line detection
Xiaolan Shen, Jiangxin Zhang, Sheng–Feng Yu, Limin Meng, Ke-Lin Du · 2012
Detecting lines correctly from a digital image is an important step in many real-world applications. It has been widely used in the fields of contour extraction, character recognition and medical image analysis, as well as in many other computer vision based applications. In this paper, we present a randomized Hough transform based line detection algorithm that utilizes the edge gradient direction. This method exploits edge gradient direction to determine the main direction of a line by applying a constraint on the randomized Hough transform. It substantially reduces the count of invalid samples in the random sampling process. The proposed sampling strategy is superior to some existing methods in terms of memory requirement and computation time.