TY - GEN
T1 - Beyond RANSAC
T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops
AU - Subbarao, Raghav
AU - Meer, Peter
PY - 2006
Y1 - 2006
N2 - RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.
AB - RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.
UR - http://www.scopus.com/inward/record.url?scp=33845529122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845529122&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2006.43
DO - 10.1109/CVPRW.2006.43
M3 - Conference contribution
AN - SCOPUS:33845529122
SN - 0769526462
SN - 9780769526461
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2006 Conference on Computer Vision and Pattern Recognition Workshop
Y2 - 17 June 2006 through 22 June 2006
ER -