Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation

We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented algorithm, called iterative MaxFS with inlier scale (IMaxFS-ISE), iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. The IMaxFS-ISE algorithm generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. A sequential "fitting-and-removing" procedure is repeated until overall energy function does not decrease. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers.

[1]  Jan-Michael Frahm,et al.  RECON: Scale-adaptive robust estimation via Residual Consensus , 2011, 2011 International Conference on Computer Vision.

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

[3]  Hongdong Li,et al.  Consensus set maximization with guaranteed global optimality for robust geometry estimation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[4]  John W. Chinneck,et al.  Feasibility And Infeasibility In Optimization , 2015 .

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

[6]  Anton Osokin,et al.  Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

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

[10]  John W. Chinneck,et al.  Feasibility and Infeasibility in Optimization:: Algorithms and Computational Methods , 2007 .

[11]  Richard I. Hartley,et al.  Global Optimization through Rotation Space Search , 2009, International Journal of Computer Vision.

[12]  Fredrik Kahl,et al.  Two View Geometry Estimation with Outliers , 2009, BMVC.

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

[14]  F. Kahl.  Multiple View Geometry and the L-infinity Norm , 2005, ICCV 2005.

[15]  Sang Wook Lee,et al.  Photometric Stereo from Maximum Feasible Lambertian Reflections , 2010, ECCV.

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

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

[18]  Frank Dellaert,et al.  GroupSAC: Efficient consensus in the presence of groupings , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  B. S. Manjunath,et al.  The multiRANSAC algorithm and its application to detect planar homographies , 2005, IEEE International Conference on Image Processing 2005.

[20]  Tat-Jun Chin,et al.  Dynamic and hierarchical multi-structure geometric model fitting , 2011, 2011 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]  Gerard Medioni,et al.  StaRSaC: Stable random sample consensus for parameter estimation , 2009, CVPR.

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

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

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

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

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

[28]  Jana Kosecka,et al.  Nonparametric Estimation of Multiple Structures with Outliers , 2006, WDV.

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

[30]  Tat-Jun Chin,et al.  Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.