特征尺度自动选择边缘分割方法

针对复杂边缘错综交织而不易分割的问题，提出了特征尺度自动选择边缘分割方法.该方法通过定义相邻两层离散尺度空间中边缘点最大偏移量，以及分析极值路径的4种演化方式，得到具有实际意义的边缘点特征尺度.根据边缘线尺度直方图和尺度空间边缘连通性，对交织的边缘进行剥离和合并，得到分割的结果.通过在多幅测试图片上的实验表明，根据尺度范围获得的边缘多尺度表达层次清晰分明，并且按照显著性提取的显著边缘较少出现细碎旁枝与实际显著边缘能较好地吻合，证明提出的边缘分割方法切实有效.

Edge Segmentation Based on Automatic Scale Selection

This paper presented a novel method to select the edge scale automatically, which could be used to split complicated interleaving edges into more meaningful edge segments that could be classified easily according to their features. First, edge extreme paths in Gaussian scale space were searched to obtain the characteristic scale for each edge point, by calculating the maximum distance the edge point travels from one scale level to adjacent one and analyzing the four forms of extreme path evolution. Then, interleaving edges were split into pieces with different scales according to their scale histogram. These edge pieces connecting with each other were combined through extreme path in scale space. Experimental results show that the proposed method is effective in edge segmentation based on characteristic scales and saliency of edge. Key words：