结合Hough变换与改进最小二乘法的直线检测

提出了一种结合Hough变换与改进的最小二乘法的直线检测方法。分析了Hough变换与最小二乘法在直线检测及检测精度上的优缺点;利用Hough变换鲁棒且不需启发式信息的特点进行初步检测,确定存在直线的大致区域;利用最小二乘法确定直线区域内特征点回归直线的精确参数。为克服最小二乘法对强噪声点敏感的缺点,提出了双点移除的p最小二乘法,通过同时剔除具有最大正负误差的一对数据点,保证数据集中的正常点能得到可靠保留以获得精确的回归结果。实验结果表明,所提方法提高了直线检测的检测率与检测精度,且降低了对Hough变换的分辨率要求,可以减小算法整体的空间开销。

Combining the Hough Transform and an Improved Least Squares Method for Line Detection

A novel line detection method combining the Hough transform and an improved least squares method was proposed. Advantages and drawbacks of the Hough transform and the least squares method on line detection and detec- lion accuracy were analyzed. Robust and heuristics-free coarse detection of lines was realized by the Hough transform to determine image regions where a line may exist. Least squares regression was then applied on feature points in these re- gions to obtain accurate line parameters. To overcome the sensitivity of conventional least squares method to outliers, the xrleast square with dual removal algorithm, which deletes each iteration a pair of data points with maximum positive and negative fitting errors to ensure the reservation of normal points in the data set and thus guarantee the accuracy of the linear regression, was proposed. Experimental results show that the novel method gives higher detection rate and more accurate line parameters compared with the Hough transform Besides, lower Hough space resolution can be a- dopted without much impacts on the detection results, thus reducing the cost on the memory needed.