A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Parameter Estimation in Computer Vision
SIAM Review
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Visualizing Incomplete and Partially Ranked Data
IEEE Transactions on Visualization and Computer Graphics
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Accelerated hypothesis generation for multi-structure robust fitting
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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
Generalized projection based M-estimator: Theory and applications
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Dynamic and hierarchical multi-structure geometric model fitting
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In many robust model fitting methods, obtaining promising hypotheses is critical to the fitting process. However the sampling process unavoidably generates many irrelevant hypotheses, which can be an obstacle for accurate model fitting. In particular, the mode seeking based fitting methods are very sensitive to the proportion of good/bad hypotheses for fitting multi-structure data. To improve hypothesis generation for the mode seeking based fitting methods, we propose a novel sample-and-filter strategy to (1) identify and filter out bad hypotheses on-the-fly, and (2) use the remaining good hypotheses to guide the sampling to further expand the set of good hypotheses. The outcome is a small set of hypotheses with a high concentration of good hypotheses. Compared to other sampling methods, our method yields a significantly large proportion of good hypotheses, which greatly improves the accuracy of the mode seeking-based fitting methods.