Performance Analysis on Stereo Matching Algorithms Based on Local and Global Methods for 3D Images Application

Nurulfajar Abd Manap, Siti Farah Hussin, Abd Majid Darsono, Masrullizam Mat Ibrahim


Stereo matching is one of the methods in computer vision and image processing. There have numerous algorithms that have been found associated between disparity maps and ground truth data. Stereo Matching Algorithms were applied to obtain high accuracy of the depth as well as reducing the computational cost of the stereo image or video. The smoother the disparity depth map, the better results of triangulation can be achieved. The selection of an appropriate set of stereo data is very important because these stereo pairs have different characteristics. This paper discussed the performance analysis on stereo matching algorithm through Peak Signal to Noise Ratio (PSNR in dB), Structural Similarity (SSIM), the effect of window size and execution time for different type of techniques such as Sum Absolute Differences (SAD), Sum Square Differences (SSD), Normalized Cross Correlation (NCC), Block Matching (BM), Global Error Energy Minimization by Smoothing Functions, Adapting BP and Dynamic Programming (DP). The dataset of stereo images that used for the experimental purpose is obtained from Middlebury Stereo Datasets.


Stereo matching; Disparity depth; Dynamic Programming; 3D imaging;

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Scharstein, D. & Szeliski, R., 2001. “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” 47(1-3), pp.7–42.

Bleyer, M., & Gelautz, M. , 2005. A layered stereo matching algorithm using image segmentation and global visibility constraints. ISPRS Journal of Photogrammetry and Remote Sensing, 59(3), pp. 128–150.

Koschan, a., Rodehorst, V., & Spiller, K., 1996. Color stereo vision using hierarchical block matching and active color illumination. Proceedings of 13th International Conference on Pattern Recognition, 1, pp. 835–839.

Donate, A., Liu, X., & Collins, E. G., 2011. Efficient path-based stereo matching with subpixel accuracy. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41(1), pp. 183– 195.

Sabatini, M., Monti, R., Gasbarri, P., & Palmerini, G. B., 2013. Adaptive and robust algorithms and tests for visual-based navigation of a space robotic manipulator. Acta Astronautica, 83, pp. 65–84.

Jodoin, P. M., Mignotte, M., & Rosenberger, C., 2007. Segmentation framework based on label field fusion. IEEE Transactions on Image Processing, 16(10), pp. 2535–2550.

Stefano, L. Di, Marchionni, M., & Mattoccia, S., 2004. A fast areabased stereo matching algorithm. Image and Vision Computing, 22(12), pp. 983–1005.

Yang, Q., 2012. A non-local cost aggregation method for stereo matching. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 1402–1409.

Li, G., 2012. Stereo Matching using Normalized Cross-Correlation in LogRGB Space. Computer Vision in Remote Sensing (CVRS), pp. 19– 23.

Fang, L., Li, S., McNabb, R. P., Nie, Q., Kuo, A. N., Toth, C. a., Farsiu, S., 2013. Fast acquisition and reconstruction of optical coherence tomography images via sparse representation. IEEE Transactions on Medical Imaging, 32(11), pp. 2034–2049.

Benzeroual, K., Allison, R. S., & Wilcox, L. M. , 2012. 3D display size matters: Compensating for the perceptual effects of S3D display scaling. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 45–52.

Cai, J. , 2007. Fast Stereo Matching : Coarser to Finer with Selective Updating Coarse to Fine Scheme Area-Based Matching. Image and Vision Computing New Zealand, pp. 266–270.

Sinha, S., Scharstein, D., & Szeliski, R., 2013. Efficient HighResolution Stereo Matching using Local Plane Sweeps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1582–1589.

Qin, X., Shen, J., Mao, X., Li, X., & Jia, Y., 2015. Structured-Patch Optimization for Dense Correspondence. IEEE Transactions on Multimedia, 17(3), pp. 295–306.

Almeida, J., Leite, N. J., & Torres, R. D. S. , 2012. VISON: VIdeo Summarization for ONline applications. Pattern Recognition Letters, 33(4), pp. 397–409.

Elboher, E., & Werman, M., 2013. Asymmetric correlation: A noise robust similarity measure for template matching. IEEE Transactions on Image Processing, 22(8), pp. 3062–3073.

Neilson, D., & Yang, Y. H., 2011. A component-wise analysis of constructible match cost functions for global stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), pp. 2147–2159.

Mei, X., Sun, X., Zhou, M., Jiao, S., & Wang, H., 2011. 2nd. On building an accurate stereo matching system on graphics hardware. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 467–474.

Michael, M., Salmen, J., Stallkamp, J., & Schlipsing, M., 2013. Realtime Stereo Vision : Optimizing Semi-Global Matching. Intelligent Vehicles Symposium (IV), pp. 1197–1202.

Felzenszwalb, P. F., & Huttenlocher, D. P., 2000. Efficient matching of pictorial structures. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2, pp. 66–73.

Kolmogorov, V., 2006. Convergent tree-reweighted message passing for energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, pp. 1568–1583.

Proenc, H., Neves, C., & Santos, G., 2013. Segmenting the Periocular Region using a Hierarchical Graphical Model Fed by Texture / Shape Information and Geometrical Constraints. IEEE International Joint Conference on Biometrics Compendium, pp. 1-7.

Savic, V., & Zazo, S., 2010. Nonparametric Belief Propagation Based on Spanning Trees for Cooperative Localization in Wireless Sensor Networks. Vehicular Technology Conference Fall (VTC 2010- Fall), 2010 IEEE 72nd, pp. 0–4.

Waggoner, J., Zhou, Y., Simmons, J., De Graef, M., & Wang, S., 2014. Graph-cut based interactive segmentation of 3D materialsscience images. Machine Vision and Applications, 25(6), pp. 1615– 1629.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 13(4), pp. 600–612.

Huat, T.C, Manap, N.A, Ibrahim, MM, 2015, Development of Double Stage Filter (DSF) for Stereo Matching Algorithms and 3D Vision Applications, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 7(2), pp. 39-46


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