Comparative Analysis of Stereo Matching Algorithms

Computer stereo vision (CSV) is the extraction of 3D data from two pictures of a similar scene taken from two parallel vantage focuses. Stereo vision has a great importance in the robotics, image analysis, object recognition etc. The correspondence problem in stereo vision refers to the problem of ascertaining similar features in stereo images. The major challenges in corresponds are due to change in viewpoints, camera noise and difference in bias. To solve this matching problem along with these challenges, many algorithms are proposed so far. This paper investigates the current stereo vision applications. The stereo vision algorithms are classified and the comparative analysis is presented. For comparison, middle bury database are used. The grapgh cut based algorithm has least bad pixel of 4.06 % and found to be best among all the available methods.

[1]  Liming Chen,et al.  A Novel Trilateral Filter based Adaptive Support Weight Method for Stereo Matching , 2013, BMVC.

[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]  Sungchul Kang,et al.  Fast correlation-based stereo matching with the reduction of systematic errors , 2005, Pattern Recognit. Lett..

[4]  F. O. Besse.  PatchMatch belief propagation for correspondence field estimation and its applications , 2013 .

[5]  Wen Gao,et al.  Local Stereo Matching with Improved Matching Cost and Disparity Refinement , 2014, IEEE MultiMedia.

[6]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[7]  Pushmeet Kohli,et al.  Surface stereo with soft segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Liming Chen,et al.  A Fast Trilateral Filter-Based Adaptive Support Weight Method for Stereo Matching , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Narendra Ahuja,et al.  A constant-space belief propagation algorithm for stereo matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Ruigang Yang,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling , 2006, CVPR.

[11]  Reinhard Männer,et al.  Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation , 2004, International Journal of Computer Vision.

[12]  Atsushi Nomura,et al.  Sub-pixel disparity search for binocular stereo vision , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[13]  Jun'ichi Takeno,et al.  Stereo vision system on mobile robots for measuring road surfaces , 1994, Adv. Robotics.

[14]  Jian Sun,et al.  Symmetric stereo matching for occlusion handling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Miao Liao,et al.  Real-time Global Stereo Matching Using Hierarchical Belief Propagation , 2006, BMVC.

[16]  Margrit Gelautz,et al.  A layered stereo algorithm using image segmentation and global visibility constraints , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[17]  Hailin Jin,et al.  A Locally Linear Regression Model for Boundary Preserving Regularization in Stereo Matching , 2012, ECCV.

[18]  Zhi-Gang Zheng,et al.  A region based stereo matching algorithm using cooperative optimization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Haidi Ibrahim,et al.  Literature Survey on Stereo Vision Disparity Map Algorithms , 2016, J. Sensors.

[20]  Cheng Lei,et al.  Region-Tree Based Stereo Using Dynamic Programming Optimization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  D. Nistér,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Li Hong,et al.  Segment-based stereo matching using graph cuts , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[23]  Sang Wook Lee,et al.  Structured-light stereo: Comparative analysis and integration of structured-light and active stereo , 2013 .

[24]  Changming Sun,et al.  Stereo matching using cost volume watershed and region merging , 2014, Signal Process. Image Commun..

[25]  Truong Q. Nguyen,et al.  Local Disparity Estimation With Three-Moded Cross Census and Advanced Support Weight , 2013, IEEE Transactions on Multimedia.

[26]  Olga Veksler,et al.  Stereo correspondence by dynamic programming on a tree , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Gérard G. Medioni,et al.  Stereo Using Monocular Cues within the Tensor Voting Framework , 2004, ECCV.

[28]  Pramode K. Verma,et al.  Dense image registration using sparse coding and belief propagation , 2011, 2011 5th International Conference on Signal Processing and Communication Systems (ICSPCS).

[29]  Yan Zhao,et al.  Improved belief propagation based on RGB vector measure for stereo matching , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

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

[31]  Hongyang Chao,et al.  As-Rigid-As-Possible Stereo under Second Order Smoothness Priors , 2014, ECCV.

[32]  Chao Liang,et al.  Stereo matching with cross-based region, hierarchical belief propagation and occlusion handling , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[33]  Songmin Jia,et al.  Robot Localization in Indoor Environments Using Radio Frequency Identification Technology and Stereo Vision , 2008, Adv. Robotics.

[34]  Antonios Gasteratos,et al.  Review of Stereo Vision Algorithms: From Software to Hardware , 2008 .

[35]  Miao Liao,et al.  High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[36]  Olga Veksler.  Reducing Search Space for Stereo Correspondence with Graph Cuts , 2006, BMVC.

[37]  Pushmeet Kohli,et al.  Object stereo — Joint stereo matching and object segmentation , 2011, CVPR 2011.

[38]  Li Xu,et al.  Stereo Matching: An Outlier Confidence Approach , 2008, ECCV.

[39]  Pramode K. Verma,et al.  Dense image registration using sparse coding and belief propagation , 2011, ICSPCS.