Probabilistic choice between symmetric disparities in motion stereo matching for a lateral navigation system

Abstract. Two consecutive frames of a lateral navigation camera video sequence can be considered as an appropriate approximation to epipolar stereo. To overcome edge-aware inaccuracy caused by occlusion, we propose a model that matches the current frame to the next and to the previous ones. The positive disparity of matching to the previous frame has its symmetric negative disparity to the next frame. The proposed algorithm performs probabilistic choice for each matched pixel between the positive disparity and its symmetric disparity cost. A disparity map obtained by optimization over the cost volume composed of the proposed probabilistic choice is more accurate than the traditional left-to-right and right-to-left disparity maps cross-check. Also, our algorithm needs two times less computational operations per pixel than the cross-check technique. The effectiveness of our approach is demonstrated on synthetic data and real video sequences, with ground-truth value.

[1]  Joost van de Weijer,et al.  Accurate Stereo Matching by Two-Step Energy Minimization , 2015, IEEE Transactions on Image Processing.

[2]  Xing Mei,et al.  On building an accurate stereo matching system on graphics hardware , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[3]  Qingxiong Yang,et al.  A non-local cost aggregation method for stereo matching , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[5]  Lance Williams,et al.  View Interpolation for Image Synthesis , 1993, SIGGRAPH.

[6]  Vladimir Kolmogorov,et al.  Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[8]  Andrew Adams,et al.  Fast High‐Dimensional Filtering Using the Permutohedral Lattice , 2010, Comput. Graph. Forum.

[9]  Goro Obinata,et al.  Vision Systems: Segmentation and Pattern Recognition , 2007 .

[10]  Yasuyuki Matsushita,et al.  Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  J. P. Lewis,et al.  Fast Template Matching , 2009 .

[12]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[13]  Federico Tombari,et al.  Near real-time stereo based on effective cost aggregation , 2008, 2008 19th International Conference on Pattern Recognition.

[14]  Tae-Sun Choi,et al.  Improved motion stereo matching based on a modified dynamic programming , 2001 .

[15]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[16]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Dmitry P. Nikolaev,et al.  UAV Navigation On The Basis Of The Feature Points Detection On Underlying Surface , 2015, ECMS.

[18]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[20]  Eric Wahl,et al.  Parking assistance using dense motion-stereo , 2011, Machine Vision and Applications.

[21]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[23]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ingemar J. Cox,et al.  A maximum-flow formulation of the N-camera stereo correspondence problem , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[25]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Minh N. Do,et al.  A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy? , 2011, 2011 International Conference on Computer Vision.

[27]  Evgeny Shvets.  Stochastic Multi-Agent Patrolling Using Social Potential Fields , 2015, ECMS.

[28]  John W. Fisher,et al.  Loopy Belief Propagation: Convergence and Effects of Message Errors , 2005, J. Mach. Learn. Res..

[29]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  D. Y. Kim,et al.  Object shape recovery in lateral navigation system using motion stereo technique , 1999, Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480).

[31]  Mikhail Mozerov.  Constrained Optical Flow Estimation as a Matching Problem , 2013, IEEE Transactions on Image Processing.

[32]  Minh N. Do,et al.  Joint Histogram-Based Cost Aggregation for Stereo Matching , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.