像素自相关矩阵的阈值自适应角点检测算法  

An adaptive threshold corner detection algorithm based on auto-correlation matrix of image pixel

作　　者：邓小炼[1] 杜玉琪 王长耀 王晓花[1] Deng Xiaolian Du Yuqi Wang Changyao Wang Xiaohua(College of Science, China Three Gorges University, Yihang 443002, China Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 1 00101, China)

机构地区：[1]三峡大学理学院,宜昌443002 [2]中国科学院遥感与数字地球研究所,北京100101

出　　处：《农业工程学报》2017年第18期134-140,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：遥感科学国家重点实验室课题(Y6Y00200KZ)

摘　　要：针对Harris角点检测算法中角点响应函数(corner response function,CRF)系数阈值与非极大值抑制系数阈值需要人为设定所造成的可变性和随机性等问题,该文提出一种通过计算图像每个像素的自相关矩阵行列式值,构造特征角点图像进行自适应阈值分割的改进Harris角点检测算法。该算法首先通过计算原图像经过方向滤波和低通滤波后各像素的自相关矩阵行列式值,以此构造特征角点图像;然后采用OTSU算法计算特征角点图像分割阈值,从而筛选出预选区域;最后结合改进的非极大值抑制方法提取有效角点。通过5组角点检测对比试验结果数据分析,不同类型图像的角点检测准确率均有提高,高分二号遥感影像的角点检测准确率提高27.06个百分点,可以初步得出,该算法相比传统Harris角点检测算法不但能够自动计算角点检测的最佳阈值,而且能够更准确地定位角点和去除边缘伪角点,从而提高了角点检测的精确度,该研究可为农业遥感影像数据检测提供参考。Harris algorithm is a classical corner detection algorithm. It can extract corners of image quickly and has a certain degree of anti-noise ability, but it has corner location error to some extent. It needs to artificially set 2 threshold parameters, and it can not easily eliminate false corners such as edge points, so it has somewhat lower accuracy of corner detection. For above-mentioned reasons, a modified Harris corner detection algorithm based on auto-correlation matrix of image pixel was proposed in this paper, and the purpose was not only to solve the problem of the variability and randomness of setting thresholds for corner response function（CRF） and non-maximum suppression in Harris algorithm, but also to improve the accuracy of corner location. In our paper, the most important innovation is embodied in 2 aspects： One is avoiding to set 2 thresholds of traditional Harris corner detection algorithm artificially, the other is locating corner more accurately by modified non-maximum suppression method. Firstly, original image was filtered by directional filtering and Gaussian low-pass filtering, and feature corner image was constructed by calculating determinant of every pixel＇s auto-correlation matrix. Potential corners of image could be heightened effectively, which had more significant intensity than other surrounding pixels, and could be recognized easily in feature corner image. Secondly, in order to improve intelligent level of the modified algorithm, we selected adaptive OTSU algorithm to determine segmentation threshold. The segmentation threshold of feature corner image could be calculated by OTSU algorithm, and the pre-selected regions were obtained. So the search range of corner detection was significantly decreased. On the basis, an optimized non-maximum suppression method was adopted in our research, which could divide each pre-selected region into several 3×3 square subranges, and correct corners were extracted from potential corners of each square subrange, false corners were eliminate

关 键 词：图像处理 算法 角点检测 自相关矩阵 特征角点图像 非极大值抑制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]