针对噪声环境下复杂工件多直线测量中存在的检测精度下降问题，提出了一种基于局部不确定性度量的直线检测算法。该算法首先将目标区域分块，在每个子块内，建立点属于某直线不确定性度量概率模型，按照两点组合原理和Bayesian法则，计算任两点所确定的直线的累积不确定度量，通过对参数空间软投票检测直线。算法具有较强的抗噪能力和快速性。实验结果表明，该算法在较高噪声（方差为0.06）时，检测精度误差小于1‰，检测正确率仍可以达到90%以上，且其检测时间是单纯不确定度量直线检测方法的1/2, 比传统Hough变换算法快4

A novel method of local uncertainty based measure is proposed for line detection in the paper to tackle the problems of decrease in detection accuracy in noised images for multi-line detection of complicated work-pieces. The proposed scheme firstly partitions an object into several regions. Then a probability model of uncertainty that describes how an edge pixel belongs to a line is built within each region, and accumulated uncertainty measures for lines formed by any pair of two edge points are computed according to two point combination and the Bayesian rule. Lines are finally detected using soft voting in parameter spaces. The capability of anti-noise and fast processing speed is the key feature of the algorithm. Experimental results show that accuracy error of proposed method less than 1‰ when noise variance equals to 0.06 and detection accuracy can reach above 90%. The detection period is 1/2 of method of pure uncertainty measure and processing speed is 4~5 times faster than conventional hough transform (HT).