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The Research of PCA-SIFT Stereo Matching Method Based on RANSAC
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liu, Aoli Wang, Haihui Xiao, Yongqiang Wang, Ziwei Zhang, Liubin Hubei, Wuhan |
| Copyright Year | 2016 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.hanspub.org/journal/PaperDownload.aspx?DOI=10.12677/JISP.2016.53014 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
