A simultaneous sample-and-filter strategy for robust multi-structure model fitting

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.

[1]  Tat-Jun Chin,et al.  Accelerated Hypothesis Generation for Multi-structure Robust Fitting , 2010, ECCV.

[2]  Jiri Matas,et al.  Locally Optimized RANSAC , 2003, DAGM-Symposium.

[3]  David W. Murray,et al.  Guided-MLESAC: faster image transform estimation by using matching priors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Charles V. Stewart,et al.  Bias in robust estimation caused by discontinuities and multiple structures , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Peter Meer,et al.  Generalized projection based M-estimator: Theory and applications , 2011, CVPR 2011.

[6]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[7]  Torsten Sattler,et al.  SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  John Mark Bishop,et al.  NAPSAC: high noise, high dimensional model parameterisation - it's in the bag , 2002 .

[9]  Guy Lebanon,et al.  Visualizing Incomplete and Partially Ranked Data , 2008, IEEE Transactions on Visualization and Computer Graphics.

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

[11]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[13]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[14]  Tat-Jun Chin,et al.  Dynamic and hierarchical multi-structure geometric model fitting , 2011, 2011 International Conference on Computer Vision.

[15]  Ronald Fagin,et al.  Comparing top k lists , 2003, SODA '03.

[16]  Tat-Jun Chin,et al.  Robust fitting of multiple structures: The statistical learning approach , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Tat-Jun Chin,et al.  Efficient Multi-structure Robust Fitting with Incremental Top-k Lists Comparison , 2010, ACCV.

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

[19]  Hiroshi Kawakami,et al.  Detection of Planar Regions with Uncalibrated Stereo using Distributions of Feature Points , 2004, BMVC.

[20]  Haifeng Chen,et al.  Robust regression with projection based M-estimators , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[21]  David Suter,et al.  Robust adaptive-scale parametric model estimation for computer vision , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Andrea Fusiello,et al.  Robust Multiple Structures Estimation with J-Linkage , 2008, ECCV.