一种提取直线的随机方法

基于Hough变换提取直线的方法，由于要预先量化参数空间，因此需要很大的存储量和计算量．基于RHT(Randomized Hough Transform)提取直线的方法是通过随机选取两个点得到直线的参数，而后在参数空间对相应的参数进行累加、判断，该方法虽然无需预先量化参数空间，但是其在直线检测时，收敛速度慢．为此提出一种新的随机检测直线(Random Line Detection)的方法，在图象边缘点构成的数据空间中随机选取3个点，根据距离准则获得一条可能的直线，然后在数据空间中进一步判断直线的真实性，实验证实了该方法能有效的减少存储空间并降低计算量。

An Efficient Random Algorithm for Lines Detection

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.