Deterministic sample consensus with multiple match hypotheses

British Machine Vision Conference, BMVC 2010 - Proceedings

Volumn , Issue , 2010, Pages

  McIlroy, Paul ^a   Rosten, Ed ^a   Taylor, Simon ^a   Drummond, Tom ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS;

AMBIGUOUS DATA; BEST MATCH; COMPUTING RESOURCE; FEATURE MATCHING; MATCHING SCORE; MODEL PARAMETERS; RANDOM SAMPLE CONSENSUS; SAMPLE CONSENSUS;

COMPUTER VISION;

EID: 84898472923     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.24.111     Document Type: Conference Paper

References (13)

1. C.M. Bishop. Pattern recognition and machine learning. Springer New York, 2006.
2. O. Chum and J. Matas. Matching with PROSAC-progressive sample consensus. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
3. M.A. Fischler and R.C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981.
4. D.G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004.
5. J. Matas and O. Chum. Randomized RANSAC with sequential probability ratio test. In Proc. IEEE International Conference on Computer Vision, 2005.
6. D.R. Myatt, P.H.S. Torr, S.J. Nasuto, J.M. Bishop, and R. Craddock. NAPSAC: high noise, high dimensional robust estimation - it's in the bag. In Proc. British Machine Vision Conference, 2002.
7. D. Nistér. Preemptive RANSAC for live structure and motion estimation. Machine Vision and Applications, 2005.
8. M. Ozuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2007.
9. E. Rosten and T.W. Drummond. Machine learning for high-speed corner detection. In Proc. European Conference on Computer Vision, 2006.
10. E. Rosten, G. Reitmayr, and T.W. Drummond. Improved RANSAC performance using simple, iterative minimal-set solvers. University of Cambridge Technical Report, 2010. URL http://xxx.lanl.gov/abs/1007.1432.
11. S.J. Taylor and T.W. Drummond. Multiple target localisation at over 100 FPS. In Proc. British Machine Vision Conference, 2009.
12. B. Tordoff and D.W. Murray. Guided sampling and consensus for motion estimation. In Proc. European Conference on Computer Vision, 2002.
13. P.H.S. Torr and A. Zisserman. MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 2000.