摘要
针对Harris算法存在运算慢、抗噪能力差以及在实际应用中存在不必要角点簇等问题,提出了一种改进的Harris角点检测算法。采用加速分割测试特征点检测原理,排除大量的非特征点得到初始点,以初始点响应Harris函数执行非极大值抑制,保留局部角点响应函数最大值的像素点,以这些点为中心,以一定半径搜索角点簇,采用容忍距离内保留一个特征点,降低角点簇影响。提取Harris角点后,采用NCC算法进行粗匹配,再用RANSAC算法消除误匹配,提高图像拼接的精度。实验结果表明,该算法能提高检测的速度,去除大量伪角点和不必要角点簇,验证了改进算法的有效性和实用性。
Harris algorithm exist the problems of high computational cost,poor noise immunity,and the presence of unnecessary corner cluster in the practical applications,so an improved Harris corner detection algorithm is proposed.The method uses the features from accelerated segment test to exclude a large number of non-feature points.Then the detected points as the initial point response function Harris performs non-maxima suppression,retain the local corner response function of maximum pixels point,and take these points as the center,in a certain corner radius search cluster,within tolerance is just only one feature point to reduce the influence of the angular point cluster.In order to improve the accuracy of image mosaic,after Harris corner is extracted,the coarse matching is done by NCC algorithm,then RANSAC algorithm is used to eliminate the false matching.The experimental results show that the improved method increases the speed of detection,removes a large number of pseudo corner points and unnecessary corner clusters.The effectiveness and feasibility of the proposed method is verified.
出处
《桂林电子科技大学学报》
2017年第5期401-405,共5页
Journal of Guilin University of Electronic Technology
基金
国家自然科学基金(61572147)
广西高校图像图形智能处理重点实验室基金(GIIP201501)
广西可信软件重点实验室基金(KX201502)
广西研究生教育创新计划(YJCXS201536)
关键词
角点检测
角点簇
非极大值抑制
图像拼接
corner point detection
angular point cluster
non-maxima suppression
image mosaic
