Deterministic Sample Consensus with Multiple Match Hypotheses

RANSAC (Random Sample Consensus) is a popular and effective technique for estimating model parameters in the presence of outliers. Efficient algorithms are necessary for both frame-rate vision tasks and offline tasks with difficult data. We present a deterministic scheme for selecting samples to generate hypotheses, applied to data from feature matching. This method combines matching scores, ambiguity and past performance of hypotheses generated by the matches to estimate the probability that a match is correct. At every stage the best matches are chosen to generate a hypothesis. This method will therefore only spend time on bad matches when the best ones have proven themselves to be unsuitable. The result is a system that is able to operate very efficiently on ambiguous data and is suitable for implementation on devices with limited computing resources.

[1]  A FischlerMartin,et al.  Random sample consensus , 1981 .

[2]  Gilman E. S. Toombes,et al.  Preemptive RANSAC for Live Structure and Motion Estimation , 2003 .

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  Vincent Lepetit,et al.  Fast Keypoint Recognition in Ten Lines of Code , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  Tom Drummond,et al.  Multiple Target Localisation at over 100 FPS , 2009, BMVC.

[7]  Jiri Matas,et al.  Randomized RANSAC with sequential probability ratio test , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  David Nistér,et al.  Preemptive RANSAC for live structure and motion estimation , 2005, Machine Vision and Applications.

[10]  Slawomir J. Nasuto,et al.  NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag , 2002, BMVC.

[11]  Tom Drummond,et al.  Improved RANSAC performance using simple, iterative minimal-set solvers , 2010, ArXiv.

[12]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[13]  David W. Murray,et al.  Guided Sampling and Consensus for Motion Estimation , 2002, ECCV.

[14]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[15]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).