针对Harris角点检测中存在角点聚簇以及阈值选择困难的问题,通过分析Harris角点检测算法的实现原理,提出了自适应非最大抑制的Harris角点检测算法.该算法首先检测角点响应函数值为局部最大值的像素点,其次对所有局部最大值进行由大到小排序并且设置一个抑制半径,通过不断减小抑制半径提取角点,有效避免了Harris角点聚簇的现象,实现Harris角点在图像空间的均匀分布.同时,该算法能够解决阈值选择困难的难题,增强了算法的适应性.实验结果表明,该算法检测出的Harris角点在空间分布更加均匀合理,能够很好的适应图像拼接、运动估计等实际应用.

Aiming at the problem involving corners clustering and the difficulty of threshold selection in Harris operator, by analyzing the theory of Harris operator, Harris corner detection algorithm based on self-adapting non-maximal suppression is proposed. Firstly, the algorithm detects the pixels which corner response function is local maximum, then local maximum Harris corners are descendant ranked and a suppression radius for each corner is set up, corners are extracted through the suppression radius continuously decreasing, corner clustering is effectively avoided and spatial even distributed over the image for the Harris corners is realized. At the same time, the algorithm is able to solve the difficult problem of threshold selection and improve the adaptability of algorithm. Experimental results show that the algorithm is able to detect corner more reasonable, and could be used for image mosaic and motion estimation, etc. quite well.