特定边界跟踪中角点检测研究

Corner point detection in interested boundary tracking

  • 摘要: 针对边界跟踪算法中在角点附近丢失边界信息的问题,对待检测边界中角点的判别和检测方法进行了研究。分析了边界搜索中所扩展的邻域的半径和角点位置的关系,提出了角点存在判据,给出了角点存在区间。提出了寻区间法角点检测算法,用5个已知边界点之间的关系,判断出角点存在的邻域,通过选取合适的邻域半径,使角点存在区间向角点收敛。对测试图像和人侧面轮廓图像的边界利用所提出的算法进行跟踪,测试图像的边界点从19个增长为37个,人侧面轮廓图像边界点从13个增长为21个,新算法准确地检测到轮廓角点和后续种子点,使边界跟踪能够反映所有的边界信息。

     

    Abstract: Aiming at the problem for losing boundary information within the region near the corner point during boundary tracking,the method for judging and searching corner point was studied. On the basis of analyzing the relationship between the radius of extended neighborhood and the location of the corner, the corner point judging rule was proposed and the range in which corner point existed was deduced. The region searching algorithm was proposed to detect the corner point in the deduced range. From the relationship of 5 known boundary points, the algorithm estimated the neighborhood in which the corner point was included, and made the neighborhood contracted to the corner point by selecting appropriate radius. Examinations were performed at test images and side face image. Results show that the boundary points increase form 13 to 21 for test image and from 19 to 37 for side face image, the new method can detect the corner point accurately and can also detect right seeds so that the boundary tracking algorithm can include all contour information.

     

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