An Efficient Random Algorithm for Lines Detection
Shen Zhen-kang · 2003
Detecting lines from a digital image is very important in computer vision. In the HT-based method, due to the fact that the parameter space is quantified, the large computation-memory requirement is needed. Randomized Hough Transform (RHT) randomly selects two pixels from an edge image to solve parameters of a line and their corresponding mapped point in the parameter space is collected by voting on the accumulator implemented by an array. In this paper, an efficient randomized algorithm for detecting lines (RLD) in image is presented. In RLD, we first randomly select three edge pixels from an edge image and define a distance criterion to determine whether there is a possible line in the image, after find a possible line we apply an evidence-collecting process to further determine whether the line is true or not. Experiments demonstrate that the proposed algorithm is valid.