International Journal of Computer Vision
An adaptive-scale robust estimator for motion estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Balanced exploration and exploitation model search for efficient epipolar geometry estimation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
In defence of RANSAC for outlier rejection in deformable registration
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Efficient and robust model fitting with unknown noise scale
Image and Vision Computing
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Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based M-estimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale parameter, in the pbM-estimator technique this scale parameter is estimated automatically from the data using kernel smoothing density estimation. In this work we improve the performance of the pbM-estimator by changing its cost function. Replacing the cost function of the pbM-estimator with the changed one yields themodified pbM-estimator. The cost function of the modified pbM-estimator is more stable relative to the scale parameter and is also a better classifier. Thus we get a more robust and effective technique. A new general method to estimate the runtime of robust regression algorithms is proposed. Using it we show, that the modified pbM-estimator runs 2 - 3 times faster than the pbM-estimator. Experimental results of fundamental matrix estimation are presented demonstrating the correctness of the proposed analysis method and the advantages of the modified pbM-estimator.