Graph Cuts Stereo Matching Based on Patch-Match and Ground Control Points Constraint

Stereo matching methods based on Patch-Match obtain good results on complex texture regions but show poor ability on low texture regions. In this paper, a new method that integrates Patch-Match and graph cuts GC is proposed in order to achieve good results in both complex and low texture regions. A label is randomly assigned for each pixel and the label is optimized through propagation process. All these labels constitute a label space for each iteration in GC. Also, a Ground Control Points GCPs constraint term is added to the GC to overcome the disadvantages of Patch-Match stereo in low texture regions. The proposed method has the advantage of the spatial propagation of Patch-Match and the global property of GC. The results of experiments are tested on the Middlebury evaluation system and outperform all the other PatchMatch based methods.

[1]  Xukun Shen,et al.  PM-PM: PatchMatch With Potts Model for Object Segmentation and Stereo Matching , 2015, IEEE Transactions on Image Processing.

[2]  Minh N. Do,et al.  Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Andrew W. Fitzgibbon,et al.  Global stereo reconstruction under second order smoothness priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Takeshi Naemura,et al.  Graph Cut Based Continuous Stereo Matching Using Locally Shared Labels , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ruigang Yang,et al.  Global stereo matching leveraged by sparse ground control points , 2011, CVPR 2011.

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

[7]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Adam Finkelstein,et al.  The Generalized PatchMatch Correspondence Algorithm , 2010, ECCV.

[9]  Krystian Mikolajczyk,et al.  Guest Editorial: Tracking, Detection and Segmentation , 2014, International Journal of Computer Vision.

[10]  Nadia Magnenat-Thalmann.  Welcome to the year 2015 , 2014, The Visual Computer.

[11]  Zhaoqi Wang,et al.  3D entity-based stereo matching with ground control points and joint second-order smoothness prior , 2014, The Visual Computer.

[12]  Carsten Rother,et al.  PatchMatch Stereo - Stereo Matching with Slanted Support Windows , 2011, BMVC.

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

[14]  Andrew Blake,et al.  LogCut - Efficient Graph Cut Optimization for Markov Random Fields , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Alois Knoll,et al.  PM-Huber: PatchMatch with Huber Regularization for Stereo Matching , 2013, 2013 IEEE International Conference on Computer Vision.

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

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

[18]  Carl Olsson,et al.  In Defense of 3D-Label Stereo , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Yong Xu,et al.  Fractal Analysis for Reduced Reference Image Quality Assessment , 2015, IEEE Transactions on Image Processing.

[21]  Jan Kautz,et al.  PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation , 2014, International Journal of Computer Vision.