Robust regression and outlier detection
Robust regression and outlier detection
Robust Parameter Estimation in Computer Vision
SIAM Review
Algorithms in C
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
SIAM Journal on Discrete Mathematics
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Visualizing Incomplete and Partially Ranked Data
IEEE Transactions on Visualization and Computer Graphics
Adaptive Sample Consensus for Efficient Random Optimization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Combining geometric and appearance priors for robust homography estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Efficient multi-structure robust fitting with incremental top-k lists comparison
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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
Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Cluster Model for robust geometric fitting
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Dynamic and hierarchical multi-structure geometric model fitting
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we show how to exploit the statistics of permutations to perform rapid hypothesis sampling for robust geometric model fitting. The permutations encapsulate data preferences for random model hypotheses, and we demonstrate that such permutations exhibit a clustering based on the membership of inliers to genuine structures in the data. We perform non-parametric mode seeking in the space of permutations, the results of which are used to derive a set of sampling distributions for minimal subset selection. Our method fully takes advantage of the allocated time by using only the most relevant subsets of the input data for hypothesis generation. Moreover it can naturally handle data with multiple structures, a condition that is usually disastrous for other methods that rely on ad hoc inlier probabilities such as keypoint matching scores. Compared to others, our method consistently returns a much lower time-to-first-solution, and median fitting error, given the same run time.