Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform...
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IEEE
2014
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Online Access: | http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf http://psasir.upm.edu.my/id/eprint/38886/ |
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my.upm.eprints.388862018-10-31T00:46:26Z http://psasir.upm.edu.my/id/eprint/38886/ Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF Mohd Suaib, Norhayati Marhaban, Mohammad Hamiruce Saripan, M. Iqbal Ahmad, Siti Anom Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) performances. The results show that SURF is outperform than SIFT in term of rate of matched points and also in computational time. IEEE 2014 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf Mohd Suaib, Norhayati and Marhaban, Mohammad Hamiruce and Saripan, M. Iqbal and Ahmad, Siti Anom (2014) Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF. In: 2014 IEEE Region 10 Symposium, 14-16 Apr. 2014, Kuala Lumpur, Malaysia. (pp. 200-203). 10.1109/TENCONSpring.2014.6863025 |
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Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) performances. The results show that SURF is outperform than SIFT in term of rate of matched points and also in computational time. |
format |
Conference or Workshop Item |
author |
Mohd Suaib, Norhayati Marhaban, Mohammad Hamiruce Saripan, M. Iqbal Ahmad, Siti Anom |
spellingShingle |
Mohd Suaib, Norhayati Marhaban, Mohammad Hamiruce Saripan, M. Iqbal Ahmad, Siti Anom Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
author_facet |
Mohd Suaib, Norhayati Marhaban, Mohammad Hamiruce Saripan, M. Iqbal Ahmad, Siti Anom |
author_sort |
Mohd Suaib, Norhayati |
title |
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
title_short |
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
title_full |
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
title_fullStr |
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
title_full_unstemmed |
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF |
title_sort |
performance evaluation of feature detection and feature matching for stereo visual odometry using sift and surf |
publisher |
IEEE |
publishDate |
2014 |
url |
http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf http://psasir.upm.edu.my/id/eprint/38886/ |
_version_ |
1643832263222231040 |
score |
13.18916 |