Features Image Matchingof SUＲF and ＲANSAC
Aiming at the problem of long running time and low matching accuracy in image matching process， random sample consensus ( ＲANSAC) algorithm is used to optimize the speed-up of robust features ( SUＲF) optimization algorithm. As a result，an adaptable algorithm is proposed to optimize image matching....
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2018-02-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1490 |
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| Summary: | Aiming at the problem of long running time and low matching accuracy in image matching process，
random sample consensus ( ＲANSAC) algorithm is used to optimize the speed-up of robust features ( SUＲF)
optimization algorithm. As a result，an adaptable algorithm is proposed to optimize image matching. Firstly，the
SUＲF operator is used for feature detection and feature description. Then the neighbor algorithm is used to prematch
the feature points. Finally，the random sampling consistency ( ＲANSAC) algorithm is used to optimize the
matching results. The scale invariant feature transform ( SIFT) algorithm，SUＲF algorithm，and the proposed
optimization algorithm are compared in the same experimental environment. Compared with the SIFT algorithm and
the SUＲF algorithm，the optimization algorithm reduces the number of matching point pairs to 38 and 18 pairs，
excluding mismatched points，improving the matching accuracy，and reducing the running time of the algorithm.
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| ISSN: | 1007-2683 |