视差生长与张量相结合的多基线稠密匹配  

Multi-baseline Dense Matching Algorithm Based on Disparity Growing and Trifocal Tensor

作　　者：胡春海[1] 张立兴[1] 孟春婵[1] 范鹏发[1] 平兆娜[1] 

机构地区：[1]河北省测试计量技术及仪器重点实验室(燕山大学),河北秦皇岛066004

出　　处：《光电工程》2013年第3期35-41,共7页Opto-Electronic Engineering

基　　金：河北省自然科学基金资助项目(F2011203117)

摘　　要：针对特征点的稠密匹配问题,提出了一种视差生长与张量相结合的多基线稠密匹配算法。利用SIFT特征提取方法在三视图中提取特征点,并用欧氏距离的方法进行粗匹配,使用所提取特征点的梯度方向特征计算梯度主方向角度差直方图,来去除误匹配,用得到的三视图匹配对计算三焦点张量和视差初值,用来引导稠密匹配。选取得到的两视图匹配点对作为根点进行视差生长,同时利用三焦点张量的点对应,得到三视图稠密匹配,并用视差初值引导匹配,以提高稠密匹配的精度。实验结果表明,本文方法计算出了稠密匹配,最后得到了精确的稠密视差图。A novel dense matching algorithm based on disparity growing and trifocal tensor is proposed because of the problem of dense matching. SIFT algorithm is used for the feature extraction of three-view. Then, the coarse matching is obtained by using the method of Euclidean distance. For removing the part incorrect matching of coarse matching set, gradient principal direction angle difference histogram of key point is used. Trifocal tensor and initial value of disparity which is used for directing dense matching is calculated by three-view matching. Root points which are selected from two-view matching points are used for disparity growing. Three-view dense matching is obtained by using point correspondence of trifocal tensor the same time. Then, initial value of disparity is used for improving the precision of the extraction. The experimental results indicate that the method get the accurate and dense matching in widely baseline case. Dense disparity map can be obtained.

关 键 词：机器视觉 稠密匹配 多基线 视差生长 三焦点张量 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]