Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/375957
Title: Beyond RANSAC: User independent robust regression
Authors: Subbarao, Raghav;Meer, Peter
subject: Beyond RANSAC: User Independent Robust Regression
Abstract: 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.
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URI: http://localhost/handle/Hannan/375957
More Information: VOLUME : 2006
Appears in Collections:2002-2008

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Title: Beyond RANSAC: User independent robust regression
Authors: Subbarao, Raghav;Meer, Peter
subject: Beyond RANSAC: User Independent Robust Regression
Abstract: 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.
Description: 

URI: http://localhost/handle/Hannan/375957
More Information: VOLUME : 2006
Appears in Collections:2002-2008

Files in This Item:
File Description SizeFormat 
AL501379.pdf1.11 MBAdobe PDFThumbnail
Preview File
Title: Beyond RANSAC: User independent robust regression
Authors: Subbarao, Raghav;Meer, Peter
subject: Beyond RANSAC: User Independent Robust Regression
Abstract: 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.
Description: 

URI: http://localhost/handle/Hannan/375957
More Information: VOLUME : 2006
Appears in Collections:2002-2008

Files in This Item:
File Description SizeFormat 
AL501379.pdf1.11 MBAdobe PDFThumbnail
Preview File