|
|
Disparity Optimization Algorithm on Sub-pixel Accuracy for Stereo Matching Using Segmentation Guided Filtering |
SHI Hua1,2, ZHU Hong1 , YU Shunyuan1 |
1.School of Automation and Information Engineering, Xi′an University of Technology, Xi′an 710048. 2.School of Science, Xi′an Technological University, Xi′an 710032 |
|
|
Abstract To solve the problems of low accuracy and staircase effect in slope and weak texture regions for local stereo matching, a disparity optimization algorithm on sub-pixel accuracy using segmentation guided filtering is proposed. Firstly, the mismatch pixels are checked on the initial stereo matching disparity map according to left-right consistency criterion, and the mismatch disparity is corrected by average filtering. Then, the guide image is obtained by segmenting the corrected disparity map, and the sub-pixel accuracy dense disparity map can be achieved via disparity optimization based on the segmentation guided filtering method. Experimental results show that by the proposed algorithm the smoothness of the disparity map in slope regions is improved effectively, the mismatch rates of the initial stereo matching disparity are reduced, and the higher precision of dense disparity is obtained.
|
Received: 18 May 2016
|
|
Fund:Supported by National Natural Science Foundation of China (No.61673318,61205155), Science and Technology Plan Project of Science and Technology Bureau in Weiyang District of Xi′an (No.201403), President Foundation of Xi′an Technological University (No.XAGDXJJ14024). |
About author:: (SHI Hua, born in 1978, Ph.D. candidate, lecturer. Her research interests include stereo vision, pattern recognition and digital image processing.) (ZHU Hong(Corresponding author), born in 1963, Ph.D., professor. Her research interests include digital image proce-ssing, intelligent video surveillance and pattern recognition.) (YU Shunyuan, born in 1982, Ph.D. candidate. Her research interests include digital image processing and pattern recognition.) |
|
|
|
[1] SCHARSTEIN D, SZELISKI R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision, 2002, 47(1): 7-42. [2] 郑志刚,汪增福.基于区域间协同优化的立体匹配算法.自动化学报, 2009, 35(5): 469-477. (ZHENG Z G, WANG Z F. A Region Based Stereo Matching Algorithm Using Cooperative Optimization. Acta Automatica Sinica, 2009, 35(5): 469-477.) [3] YOON K J, KWEON I S. Adaptive Support-Weight Approach for Correspondence Search. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006, 28(4): 650-656. [4] 李德广,李科杰,高丽丽.基于多尺度多方向相位匹配的立体视觉方法.仪器仪表学报, 2004, 25(4S): 600-602. (LI D G, LI K J, GAO L L. Stereo Vision Using Multiresolution and Multiorientation Phase Matching. Chinese Journal of Scientific Instrument, 2004, 25(4S): 600-602.) [5] NALPANTIDIS L, GASTERATOS A. Biologically and Psychophysically Inspired Adaptive Support Weights Algorithm for Stereo Correspondence. Robotics and Autonomous Systems, 2010, 58(5): 457-464. [6] DE-MAEZTU L, VILLANUEVA A, CABEZA R. Stereo Matching Using Gradient Similarity and Locally Adaptive Support-Weight. Pattern Recognition Letters, 2011, 32(13): 1643-1651. [7] VEKSLER O. Fast Variable Window for Stereo Correspondence Using Integral Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2003, I: 556-561. [8] FUSIELLO A, ROBERTO V, TRUCCO E. Efficient Stereo with Multiple Windowing // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washingtion, USA: IEEE, 1997: 858-863. [9] KANG S B, SZELISKI R, CHAI J X. Handling Occlusions in Dense Multi-view Stereo // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washingtion, USA: IEEE, 2001: 103-110. [10] 曹晓倩,马彩文.基于最优斜面参数估计的局部立体匹配算法.红外与激光工程, 2014, 43(3): 973-978. (CAO X Q, MA C W. Best Slant-Plane Estimation Based Stereo Matching Algorithm. Infrared and Laser Engineering, 2014, 43(3): 973-978.) [11] BOBICK A F, INTILLE S S. Large Occlusion Stereo. International Journal of Computer Vision, 1999, 33(3): 181-200. [12] DE-MAEZTU L, MATTOCCIA S, VILLANUEVA A. Linear Stereo Matching // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2011: 1708-1715. [13] 时 华,朱 虹.基于自适应匹配窗及多特征融合的立体匹配.模式识别与人工智能, 2016, 29(3): 193-203. (SHU H, ZHU H. Stereo Matching Based on Adaptive Matching Windows and Multi-feature Fusion. Pattern Recognition and Artificial Intelligence, 2016, 29(3): 193-203.) [14] DE-MAEZTU L, VILLANUEVA A, CABEZA R. Near Real-Time Stereo Matching Using Geodesic Diffusion. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(2): 410-416. [15] YANG Q X, YANG R G, DAVIS J, et al. Spatial-Depth Super-Resolution for Range Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2007: 1-8. [16] BLEYER M, ROTHER C, KOHLI P. Surface Stereo with Soft Segmentation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 1570-1577. [17] ZHANG Y L, GONG M L, YANG Y H. Local Stereo Matching with 3D Adaptive Cost Aggregation for Slanted Surface Modeling and Sub-pixel Accuracy[C/OL]. [2016-04-28]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.214.3610&rep=rep1&type=pdf. [18] HE K M, SUN J, TANG X O. Guided Image Filtering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. [19] ZHAN Y L, GU Y Z, HUANG K, et al. Accurate Image-Guided Stereo Matching with Efficient Matching Cost and Disparity Refinement. IEEE Trans on Circuits and Systems for Video Technology, 2015, 26(9): 1632-1645. [20] PENG Y, LI G, WANG R G, et al. Stereo Matching with Space-Constrained Cost Aggregation and Segmentation-Based Disparity Refinement. Proc of SPIE, 2015. DOI: 10.1117/12.2083741. [21] KORDELAS G A, ALEXIADIS D S, DARAS P, et al. Enhanced Disparity Estimation in Stereo Images. Image and Vision Computing, 2015, 35: 31-49. [22] JIAO J B, WANG R G, WANG W M, et al. Local Stereo Mat-ching with Improved Matching Cost and Disparity Refinement. IEEE MultiMedia, 2014, 21(4): 16-27. [23] YANG Q X. Stereo Matching Using Tree Filtering. IEEE Trans on Pattern Analysis and Machine Intelligence, 2015, 37(4): 834-846. [24] YANG Q X. A Non-local Cost Aggregation Method for Stereo Mat-ching // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washingtion, USA: IEEE, 2012: 1402-1409. [25] LIU T L, DAI X B, HUO Z Y, et al. A Cost Construction via MSW and Linear Regression for Stereo Matching // Proc of the 21st International Conference on Pattern Recognition. New York, USA: IEEE, 2012: 914-917. [26] WANG L Q, LIU Z, ZHANG Z H. Feature Based Stereo Matching Using Two-Step Expansion. Mathematical Problems in Enginee-ring, 2014. DOI: 10.1155/2014/452803. [27] WANG Y L, DUNN E, FRAHM J M. Increasing the Efficiency of Local Stereo by Leveraging Smoothness Constraints // Proc of the 2nd International Conference on 3D Imaging, Modeling, Proce-ssing, Visualization and Transmission. Washington, USA: IEEE, 2012: 246-253. [28] PLOUMPIS S, AMANATIADIS A, GASTERATOS A. A Stereo Matching Approach Based on Particle Filters and Scattered Control Landmarks. Image and Vision Computing, 2015, 38: 13-23. [29] WANG L, YANG R G, GONG M L, et al. Real-Time Stereo Using Approximated Joint Bilateral Filtering and Dynamic Progra-mming. Journal of Real-Time Image Processing, 2014, 9(3): 447-461. [30] MICHAEL M, SALMEN J, STALLKAMP J, et al. Real-Time Stereo Vision: Optimizing Semi-global Matching // Proc of the IEEE Intelligent Vehicles Symposium. Washington, USA: IEEE, 2013: 1197-1202. [31] SAMADI M, OTHMAN M F. A New Fast and Robust Stereo Mat-ching Algorithm for Robotic Systems // Proc of the 9th International Conference on Computing and Information Technology. Berlin, Germany: Springer, 2013: 281-290. [32] MARTINS J A, RODRIGUES J M F, DU BUF H. Luminance, Colour, Viewpoint and Border Enhanced Disparity Energy Model. PLoS One, 2015, 10(6): e0129908. [33] MANAP N A, SORAGHAN J J. Disparity Refinement Based on Depth Image Layers Separation for Stereo Matching Algorithms. Journal of Telecommunication, Electronic and Computer Enginee-ring, 2012, 4(1): 51-64. |
|
|
|