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Improved PCA-SIFT Algorithm for Matching Stereo System
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhijing, 于之靖 Yu Shaobin, 王韶彬 Wang |
| Copyright Year | 2016 |
| Abstract | Aim at stereo matching problem in binocular vision measurement, a binocular vision stereo matching method based on principal component analysis(PCA) algorithm and scale invariant feature transform(SIFT) algorithm is put forward. The system uses GSI code as its feature points, and the binocular camera imaging on code identification plate using speckle as its background. Then a new algorithm combining the PCA with the SIFT is used to extract feature and solve matching problem on the acquisition images. We can achieve accurate extraction and stereo matching on these GSI code points and measure the precise position and orientation under different displacement between the feature points. The experimental verification part introduced the GSI coding technique and it is carried at 1000 mm×1000 mm stroke dimensional precision translation stage. Actual measurement of displacement got the absolute error within 5×10 mm, which can verify the high accuracy of the proposed system. |
| Starting Page | 031501 |
| Ending Page | 031501 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| DOI | 10.3788/LOP53.031501 |
| Volume Number | 53 |
| Alternate Webpage(s) | http://www.opticsjournal.net/ViewFull0.htm?aid=OJ160304000060IeKhNj |
| Alternate Webpage(s) | http://www.opticsjournal.net/ViewFullPDF.htm?aid=OJ160304000060IeKhNj |
| Alternate Webpage(s) | https://doi.org/10.3788/LOP53.031501 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
