改进的RANSAC立体匹配算法的研究  

Research on Stereo Matching of Improved RANSAC Algorithm

作　　者：孔祥思 

机构地区：[1]北京建筑大学测绘与城市空间信息学院,北京100044

出　　处：《北京建筑大学学报》2017年第4期39-44,共6页Journal of Beijing University of Civil Engineering and Architecture

基　　金：国家自然科学基金项目(40771178)

摘　　要：图像匹配的精度在很大程度上决定着三维重建的成功与否.为了提高图像匹配的精度和速度,提出了一种改进的随机抽样一致性(RANSAC)算法的图像匹配方法.该方法首先采用Harris角点检测算子提取图像的特征点,然后通过SIFT算法计算特征描述子对图像进行初匹配,最后采用改进的RANSAC算法对误匹配点进行剔除,在保证精度的前提下提高了算法的速度.主要从两个方面进行RANSAC算法的改进:采用均匀分布9点算法生成的基本矩阵代替常规方法中的单应矩阵作为模型进行计算,使得模型具有较高的鲁棒性;使用随机块选取法选择样本,保障了选点的均匀分布性并且保证了精度.实验结果表明,此方法不仅能够得到较高的精确度,而且还大幅度减少了计算量,提高了匹配速度.The accuracy of image matching determines the success of 3D reconstruction to a great extent. In this paper, an image matching method based on modified RANSAC algorithm is proposed to improve the precision and speed. Firstly, the feature points of the images are extracted using the Harris algorithm. Then, the image pair is matched roughly by generating SIFT feature descriptor. At last, the precision of image matching is optimized by the modified RANSAC algorithm, which improved the speed of calculation under the premise of ensuring accuracy. The RANSAC algorithm is improved from the following two aspects ： uses the fundamental matrix generated by the 9 point algorithm instead of the homography matrix in the traditional RANSAC algorithm as the model, which makes the model more robust and suitable for the entire data set; the sample is selected by a random block selecting method, which ensures the uniform distribution and the accuracy. The experimental results show that this method can not only get higher matching accuracy, but also greatly reduce the computation and improve the matching speed.

关 键 词：HARRIS算子 图像匹配 随机抽样一致性(RANSAC) 基本矩阵 随机块选取 

分 类 号：P237[天文地球—摄影测量与遥感]