TY - GEN
T1 - Separable four points fundamental matrix
AU - Ben-Artzi, Gil
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - We present a novel approach for RANSAC-based computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it directly induces a consecutive sampling strategy of two independent sets of correspondences. We show that our method guarantees a minimal number of evaluated hypotheses with respect to current minimal approaches, on the condition that there are four correspondences on an image line. We validate our approach on real-world image pairs, providing fast and accurate results.
AB - We present a novel approach for RANSAC-based computation of the fundamental matrix based on epipolar homography decomposition. We analyze the geometrical meaning of the decomposition-based representation and show that it directly induces a consecutive sampling strategy of two independent sets of correspondences. We show that our method guarantees a minimal number of evaluated hypotheses with respect to current minimal approaches, on the condition that there are four correspondences on an image line. We validate our approach on real-world image pairs, providing fast and accurate results.
UR - http://www.scopus.com/inward/record.url?scp=85116082325&partnerID=8YFLogxK
U2 - 10.1109/WACV48630.2021.00023
DO - 10.1109/WACV48630.2021.00023
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AN - SCOPUS:85116082325
T3 - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
SP - 188
EP - 196
BT - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Y2 - 5 January 2021 through 9 January 2021
ER -