基于最小二乘修正的随机Hough变换直线检测

利用Hough变换进行直线检测时,由于直线在参数空间中的映射容易受到邻近目标、噪声以及本身非理想状态的干扰,算法中的投票过程较易出现无效累积,进而导致虚检、漏检及端点定位不准等问题.针对传统方法的上述缺陷,提出了一种基于 ρ-θ 域最小二乘拟合修正的随机Hough变换的直线检测方法.首先, 在随机抽样时利用像素-长度比值对抽样的有效性进行判定,剔除不在直线上的抽样点对;然后, 对邻域相关点进行 ρ-θ 域的最小二乘拟合,得到修正后的直线参数用于累加投票,投票过程中设定累加阈值,通过检测峰值点逐次检出疑似长直线;最后, 通过设定断裂阈值对每条长直线进行筛选和分段,定位出直线段的端点.仿真实验表明,所提方法在投票时有效抑制了复杂环境对局部最大值的干扰,使直线检测的准确率得到显著提升.

Randomized Hough transform straight line detection based on least square correction

When applying Hough transform to straight line detection, a straight line's mapping model can easily be interfered by the other lines, short segment noise or its own un-ideality in the parameter space, which brings invalid votings leading to problems such as fault detection, missed detection and inaccurate endpoint location. A novel method was proposed which introduced Least Square Method (LSM) performed in ρ-θ domain to Random Hough Transform (RHT) algorithm for detecting straight lines. The validity of sample was verified by pixel-length ratio before each voting in order to get rid of pseudo lines, which was followed by linear fitting based on least square method in parameter space for parameter correction. By setting an accumulation threshold, straight line candidates were picked out one by one via detecting peak point. Endpoints of straight line segments were located by setting a gap-and-scale threshold. The method is proved to have higher detecting precision than conventional Hough transform.