Binary stereo matching

In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem. The cost volume is constructed through bitwise operations on a series of binary strings. Then this approach is combined with traditional winner-take-all strategy, resulting in a new local stereo matching algorithm called binary stereo matching (BSM). Since core algorithm of BSM is based on binary and integer computations, it has a higher computational efficiency than previous methods. Experimental results on Middlebury benchmark show that BSM has comparable performance with state-of-the-art local stereo methods in terms of both quality and speed. Furthermore, experiments on images with radiometric differences demonstrate that BSM is more robust than previous methods under these changes, which is common under real illumination.

[1]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

[2]  Stefano Mattoccia,et al.  Linear stereo matching , 2011, 2011 International Conference on Computer Vision.

[3]  YangRuigang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007 .

[4]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Federico Tombari,et al.  Classification and evaluation of cost aggregation methods for stereo correspondence , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

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

[10]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[11]  Adam Finkelstein,et al.  PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.

[12]  KweonIn So,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006 .

[13]  Margrit Gelautz,et al.  Local stereo matching using geodesic support weights , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

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

[16]  Ruigang Yang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007, International Journal of Computer Vision.