INFORMATION CHANGE THE WORLD

International Journal of Engineering and Manufacturing(IJEM)

ISSN: 2305-3631 (Print), ISSN: 2306-5982 (Online)

Published By: MECS Press

IJEM Vol.6, No.4, Jul. 2016

A Survey on Stereo Matching Techniques for 3D Vision in Image Processing

Full Text (PDF, 345KB), PP.40-49


Views:200   Downloads:11

Author(s)

Deepika Kumari, Kamaljit Kaur

Index Terms

Stereo Matching;Disparity Map;Guided Filter

Abstract

Extraction of three-dimensional scene from the stereo images is the most effective research area in the field of computer vision. Stereo vision constructs the actual three-dimensional scene from two stereo images having different viewpoints. Stereo matching is a correspondence problem, that means it ascertains which part of image corresponds to which part of another image ,where variations inside two images is due to the movement of camera or elapse of time. Many stereo matching algorithms have been developed in order to construct the accurate disparity map. This paper presents a review on various stereo matching techniques. The comparison among existing techniques has clearly shown that none perform optimistically every time. This review has shown that the existing methods in stereo matching involve median filtering. But median filter is not effective for high density of noise. Besides mean-shift segmentation is being used for disparity refinement in existing methods, which can be enhanced by using improved mean-shift segmentation, available now days. In addition, guided filter has been used by many algorithms, but this can be replaced by joint trilateral filters. 

Cite This Paper

Deepika Kumari, Kamaljit Kaur,"A Survey on Stereo Matching Techniques for 3D Vision in Image Processing", International Journal of Engineering and Manufacturing(IJEM), Vol.6, No.4, pp.40-49, 2016.DOI: 10.5815/ijem.2016.04.05

Reference

[1]D. Scharstein, R. Szeliski, "A taxonomy and evaluation of dense two frame stereo correspondence algorithms", in Int. J. Comp. Vis. 47 (2002) 7–42.

[2]J. Sun, N. Zheng, H.Y. Shum, "Stereo matching using belief propagation", in IEEE Transactions on Pattern Analysis Machine Intelligence 25 (7) (2003) 787–800.

[3]W.Daolei, K.B.Lim, "Obtaining depth maps from segment based stereo matching using graph cuts", in J.Vis.Commun.Image R.22 (2011)325-331.

[4]Y.Zhou, C.Hou, "Stereo matching based on guided filter and segmentation", in Optik 126(2015) 1052-1056.

[5]Q.Yang, P.Ji, D.Li, S.Yao, M.Zhang, "Fast Stereo Matching using adaptive guided filtering, in Image and vision computing 32(2014) 202-211.

[6]L.Nalpantidis,G.Ch.Sirakaulis, "Review of stereo algorithms on 3D vision", in 16th International Symposium on measurement and control in robotics (2007),pp. 116-124.

[7]Q. Yang, L. Wang, R. Yang, H. Stewenius, D. Nister, "Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling",IEEE Trans. Pattern Anal. Mach. Intell. 31 (2009) 492–504.

[8]Q.Luo, J.Zhou, S.Yu, D.Xiao, "Stereo matching and occlusion detection with integrity and illusion sensitivity", Pattern recognition letters 24(2003) 1143-1149.

[9]F.Cheng, H.Zhang, M.Sun, D.Yuan, "Cross-trees, edges and super-pixel priors-based cost aggregation for stereo matching", in Pattern Recognition 48 (2015) 2269-2278.

[10]K. Andreas, M. Sormann, K. Karner, "Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure", in: International Conference on Pattern Recognition, vol. 3, 2006, pp. 15–18.

[11]F. Cheng, H.Zhang, D.Yuan, M.Sun, "Stereo matching by using the global edge constraint", in Neurocomputing (2013).

[12]X.Wang, H.Wang,Y.Su , "Accurate belief propogation with parametric and non-parametric measures for stereo-matching", in Optik 126(2015) 545-550.

[13]F.Da,F.He,Z.Chen, "Stereo Matching based on dissimilar intensity support and belief propogation", in J Math Imaging Vis (2013) 47:27–34.

[14]F. Tombari, S. Mattoccia, L. Di Stefano, E. Addimanda, "Classification and evaluation of cost aggregation methods for stereo correspondence", in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.

[15]H.Han,X.Han,F.Yang, "An improved gradient-based dense stereo correspondence algorithm using guided filter",in Optik125 (2014) ,pp.115– 120.

[16]F.Tombari,S.Mattoccia,L.Stefano Di, "Segmentation-Based Adaptive Support for Accurate Stereo Correspondence", in PSIVT 2007, LNCS 4872, pp. 427–438, 2007.

[17]O. Veksler, " Stereo correspondence by dynamic programming on a tree", in: IEEE International Conference on Computer Vision and Pattern Recognition, 2005,pp. 384–390.

[18]R.Elias, "Sparse view Stereo Matching" , in Pattern Recognition Letters 28 (2007) 1667–1678. 

[19]K.He,J.Sun,X.Tang, "Guided Image Filtering", in IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,VOL.35,NO.6.

[20]D. Comaniciu, P. Meer, "Mean shift: a robust approach toward feature space analysis", in Pattern Anal. Mach. Intell. 24 (2002) 603–619.

[21]H. Hirchmuller, "Improvements in Real-Time Correlation-Based Stereo Vision",in Proceedings of IEEE Workshop on Stereo and Multi-Baseline Vision, 2001, pp 141-148.

[22]Z .Zhang; C.Hou; Jing Shi, "A Research on stereo matching algorithm based on edge detection and gaussian disparity distribution model", in Computer Science and Information Technology, 2009. pp.650-653.

[23]Y.Yin,M.Jin,S.Xie Yi,"A Stereo Pairs Disparity Matching Algorithm by Mean-Shift Segmentation", in Third International Workshop on Advanced Computational Intelligence,2010,pp.639-642.

[24]Z.Youlian,H.Cheng, "An Improved Median Filtering Algorithm Combined with Average Filtering", in Third International Conference on Measuring Technology and Mechatronics Automation,2011,pp.420-423.

[25]J.Zhou,Z.Li,C.Fan, "Improved fast mean shift algorithm for remote sensing image segmentation",in IET Image Process., 2015, Vol. 9, Iss. 5, pp. 389–394.

[26]M.Stoll,S.Volz,A.Bruhn, "Joint Trilateral Filtering For Multiframe Optical Flow", in Image Processing (ICIP), 2013 20th IEEE International Conference on , vol., no., pp.3845-3849, 15-18 Sept. 2013

[27]Z.Qian,C.Zhu,R.Wang, "An Improved Fast Mean Shift Algorithm for Segmentation",in International Conference on Computer Application and System Modeling (ICCASM 2010),vol.6,pp.116-120.

[28]K.Muhlmann, D.Maier, J.Hesser and R.Manner , "Calculating dense disparity maps from color stereo images,an Efficient Implementation",IJCV 47(2002),1/2/3,pp.79-88.

[29]L.D.Stefano,M.Marchionni,S.Mattoccia, "A Fast area-based stereo matching algorithm",Image and Vision computing 22(2004),pp.983-1005.

[30]E.Binaghi,I.Gallo,G.Marino,M.Raspanti, "Neural Adaptive Stereo Matching",Pattern Recognition Letters 25(2004),pp.1743-1758.

[31]A.S.Ogale,Y.Aloimonos, "Shape and the Stereo correspondence problem", IJCV 65(2005),3,pp.147-162.

[32]S.Yoon,S.K.Park,Y.K.Kwak, "Fast correlation-based stereo matching with the reduction of systematic errors",Pattern Recognition Letters 26(2005),pp.2221-2231.

[33]K.Yoon,I.So.Kweon, "Adaptive Support-weight approach for correspondence search", IEEE Transactions on Pattern Analysis and Machine Intelligence(2006),vol.28,no.4.

[34]P.H.S.Torra, A.Criminisi, "Dense stereo using pivoted dynamic programming",Image and Vision Computing 22(2004),pp.795-806.

[35]M.Bleyer,M.Gelautz, " A layered stereo matching algorithm using image segmentation and global visibility constraints",ISPRS Journal of Photogrammetry & Remote Sensing 59(2005),pp.128-150

[36]O.Veksler, "Extracting dense features for visual correspondence with graph cuts",Proceedings of the IEEE Computer Society Conference on Computer vision and Pattern Recognition (2003).

[37]M.Gong,Y.H.Yang, " Fast stereo matching using reliability based dynamic programming and consistency constraints",Proceedings of ninth IEEE International Conference on computer vision(2003).

[38]C. L. Zitnick and T. Kanade, "A Cooperative Algorithm for Stereo Matching and Occlusion Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no.7, 2000.

[39]J. Sun, H. Y. Shum and N. N. Zheng, "Stereo Matching Using Belief Propagation,"Proc. European Conf. Computer Vision (2002), pp. 510-524.

[40]V. Komolgorov and R. Zabih, "Computing Visual Correspondence with Occlusions using Graph Cuts", Proc. Int'l Conf. Computer Vision(2002).

[41]Y. Boykov, O. Veksler and R. Zabih, "Fast Approximate Energy Minimization via GraphCuts" ,IEEE Trans. Pattern Analysis and Machine Intelligence(2001), vol. 23, no. 11, pp. 1222-1239.

[42]S. Birchfield and C. Tomasi, "Depth discontinuities by pixel-to-pixel stereo", In ICCV(1998), pages 1073–1080. 

[43]Y. Boykov, O. Veksler, and R. Zabih, " A variable window approach to early vision", IEEE TPAMI(1998), 20(12):1283–1294.

[44]H. Ishikawa and D. Geiger, " Occlusions, discontinuities, and epipolar lines in stereo", In ECCV(1998), pages 232–248.

[45]D. Scharstein and R. Szeliski., 'Stereo matching with nonlinear diffusion" IJCV(1998), 28(2):155–174.

[46]S.Gutierrez,J.L.Marroquin, "Robust Approach for disparity estimation in stereo vision ",Image and Vision Computing 22(2004),pp.183-195.

[47]Q.Zheng,S.Li,Y.Zhang,P.Wang and J.F.Huang, " New stereo matching method based on BP algorithm", International Journal On Smart Sensing And Intelligent Systems(2015), Vol. 8, No. 1.

[48]S. Birchfield and C. Tomasi, "Multiway Cut for Stereo and Motion with Slanted Surfaces," Proc. Int'l Conf. Computer Vision(1999), vol. 1, pp. 489-495.

[49]V. S. Kluth, G. W. Kunkel, and U. A. Rauhala, "Global Least Squares Matching," Proc. Int'l Geoscience and Remote Sensing Symp.(1992), vol. 2, pp. 1615-1618.

[50]B. D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Int'l Joint Conf. A. I., pp. 674-679, 1981.

[51]M.Z.Brown, D. Burschka, G.D. Hager, "Advances in computational stereo," in Pattern Analysis and Machine Intelligence, IEEE Transactions on(2003) , vol.25, no.8, pp.993-1008.

[52]B. K. P. Horn and B. G. Schunk, "Determining Optical Flow," Artificial Intelligence(1981), vol.17, pp. 185-204.

[53]M.Gosta and M.Grgic, "Accomplishments and Challenges of Computer Stereo Vision," 52nd international symposium ELMAR-2010(2010) ,pp.57-64.

[54]Z. Yongqin, C.Hui, W. Ling, X.Yongjun, H.Haibo, " Color Image Segmentation Using Level Set Method With Initialization Mask in Multiple Color Spaces", I.J. Engineering and Manufacturing 2011, 4, 70-76.