基于Shi-Tomasi角点验证的线段提取算法优化方法

针对现有的线段提取算法在图像中的天空、阴影、玻璃以及地板等模糊区域提取出较多的无意义线段的问题，提出了一种基于Shi-Tomasi角点验证的线段提取算法优化方法(ST-Lines算法):首先，使用经典线段提取算法进行线段提取；然后，采用Shi-Tomasi角点检测算法提取角点，并利用滑动窗口对所得的角点进行非极大值抑制；最后，根据线段长度、线段端点圆形框内的角点分布情况以及K最近邻算法对每条线段进行有无意义验证，尽可能多地剔除无意义线段。并利用YorkUrban线段数据集，对ST-Lines算法与原线段提取方法进行测试对比。对比结果表明:ST-Lines算法在平均准确率、F-score、平均线段长度上有所提高，且降低了平均线段数量。

Line Segment Extraction Algorithm Optimization Based on Shi-Tomasi Corner Detector

The existing line segment extraction algorithm extracts many meaningless lines from blurred areas such as the sky, shadow, glass and floor in the image. To solve this problem, a method for line segment extraction algorithm optimization based on Shi-Tomasi corner detector is proposed, which is called ST-Lines. It firstly uses a classic line segment extraction algorithm to extract lines. Then corners are extracted with the Shi-Tomasi corner detector and non-maximum suppression is applied to them with a sliding window. Finally, it verifies the meaning of each line segment and removes as many meaningless line segments as possible to optimize the line segment extraction algorithm based on the length of the line segment, the distribution of corners in the circle region of endpoints, and the K-nearest neighbor algorithm. The YorkUrban line segment dataset is used to compare ST-Lines algorithm and the original algorithm. The experimental results show that ST-Lines algorithm can improve the accuracy, F-score and the averageline length and reduce the average line number.