基于RANSAC的PCA-SIFT立体匹配方法研究

The Research of PCA-SIFT Stereo Matching Method Based on RANSAC

摘要: 针对传统的SIFT (Scale-Invariant Feature Transform)匹配算法数据量大，耗时长的问题，采用主成分不变特征变换PCA-SIFT (Principal Component Analysis, PCA)匹配算法。PCA-SIFT匹配算法将传统SIFT算法中的直方图法换做主元分析法，降低了传统SIFT特征描述符的维数，减少了数据量，提高了匹配效率。首先提取出两幅待匹配图像中的所有特征描述子，其次将提取出的特征描述子采用距离比阈值筛选出匹配点对，再采用RANSAC (Random Sample Consensus)法消除错配，最后得到精确的匹配结果。实验结果表明，PCA-SIFT + RANSAC算法较稳定、精确、快速。

Abstract: Principal Component Analysis of Traditional Scale-Invariant Feature Transform (PCA-SIFT) is used instead of Traditional Scale-Invariant Feature Transform (SIFT) method which has a large amount of data, and needs long time. Principal Component Analysis of Traditional Scale-Invariant Feature Transform (PCA-SIFT) changed histogram method for main element analysis method, effectively reducing the dimension of the feature descriptor, decreasing data, improving the matching rate. Firstly we extract all the feature descriptors from the two matching images, and match them with the enulidean distance ratio, and then we use the Random Sample Consensus (RANSAC) algorithm to remove false matching. The experimental results show that the PCA-SIFT + RANSAC algorithm is more stable, more accurate and more rapid.