A New Approach to Robust Estimation of Parametric Structures

Most robust estimators require tuning the parameters of the algorithm for the particular application, a bottleneck for practical applications. The paper presents the Multiple Input Structures with Robust Estimator (MISRE), where each structure, inlier or outlier, is processed independently. The same two constants are used to find the scale estimates over expansions for each structure. The inlier/outlier classification is straightforward since the data is processed and ordered with the relevant inlier structures listed first. If the inlier noises are similar, MISRE's performance is equivalent to RANSAC-type algorithms. MISRE still returns the correct inlier estimates when inlier noises are very different, while RANSAC-type algorithms do not perform as well. MISRE's failures are gradual when too many outliers are present, beginning with the least significant inlier structure. Examples from 2D images and 3D point clouds illustrate the estimation.

[1]  Jan-Michael Frahm,et al.  USAC: A Universal Framework for Random Sample Consensus , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Tat-Jun Chin,et al.  Robust Fitting in Computer Vision: Easy or Hard? , 2018, ECCV.

[3]  Brendan J. Frey,et al.  FLoSS: Facility location for subspace segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[4]  Tal Hassner,et al.  When standard RANSAC is not enough: cross-media visual matching with hypothesis relevancy , 2013, Machine Vision and Applications.

[5]  Jan-Michael Frahm,et al.  A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus , 2008, ECCV.

[6]  Zhenwei Cao,et al.  Robust Model Fitting Using Higher Than Minimal Subset Sampling , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Eric Brachmann,et al.  DSAC — Differentiable RANSAC for Camera Localization , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Stefano Soatto,et al.  KALMANSAC: robust filtering by consensus , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[10]  René Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.

[11]  Yuri Boykov,et al.  Energy-Based Geometric Multi-model Fitting , 2012, International Journal of Computer Vision.

[12]  Luc Van Gool,et al.  Consensus Maximization with Linear Matrix Inequality Constraints , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[15]  Andrea Fusiello,et al.  Structure-and-motion pipeline on a hierarchical cluster tree , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[16]  Xiang Yang,et al.  Robust method in photogrammetric reconstruction of geometric primitives in solid modeling , 2017 .

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

[18]  Vincent Lepetit,et al.  Combining Geometric and Appearance Priors for Robust Homography Estimation , 2010, ECCV.

[19]  Tat-Jun Chin,et al.  Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC , 2011, NIPS.

[20]  Hassan Foroosh,et al.  Improving RANSAC-Based Segmentation through CNN Encapsulation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Yasuyuki Matsushita,et al.  GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Peter Meer,et al.  Generalized Projection-Based M-Estimator , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Jiri Matas,et al.  Graph-Cut RANSAC , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Ruwan Tennakoon,et al.  Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting , 2017, IEEE Transactions on Image Processing.

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

[26]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[27]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  S. Basah,et al.  Conditions for motion-background segmentation using fundamental matrix , 2009, DICTA 2009.

[29]  Tat-Jun Chin,et al.  A global optimization approach to robust multi-model fitting , 2011, CVPR 2011.

[30]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[31]  G. Medioni,et al.  StaRSaC: Stable random sample consensus for parameter estimation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[33]  Huu Le,et al.  An Exact Penalty Method for Locally Convergent Maximum Consensus , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Lionel Moisan,et al.  Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers , 2012, Image Process. Line.

[35]  Richard I. Hartley,et al.  Theory and Practice of Projective Rectification , 1999, International Journal of Computer Vision.

[36]  Jan-Michael Frahm,et al.  Structure-from-Motion Revisited , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Kenichi Kanatani,et al.  Ellipse Fitting with Hyperaccuracy , 2006, IEICE Trans. Inf. Syst..

[38]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Sunglok Choi,et al.  Performance Evaluation of RANSAC Family , 2009, BMVC.

[40]  Tat-Jun Chin,et al.  The Random Cluster Model for robust geometric fitting , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Mohcine Chraibi,et al.  Calculating ellipse overlap areas , 2011, Comput. Vis. Sci..

[42]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[43]  Simon Korman,et al.  Latent RANSAC , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[44]  Zygmunt L. Szpak,et al.  Guaranteed Ellipse Fitting with a Confidence Region and an Uncertainty Measure for Centre, Axes, and Orientation , 2015, Journal of Mathematical Imaging and Vision.

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

[46]  Andrea Fusiello,et al.  T-Linkage: A Continuous Relaxation of J-Linkage for Multi-model Fitting , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[47]  Andrew Zisserman,et al.  Geometric Grouping of Repeated Elements within Images , 1998, BMVC.

[48]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[49]  Tat-Jun Chin,et al.  Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Wolfgang Förstner,et al.  Direct Solutions for Computing Cylinders from Minimal Sets of 3D Points , 2006, ECCV.

[51]  Alexander M. Bronstein,et al.  Inverting RANSAC: Global model detection via inlier rate estimation , 2015, CVPR.