ISVC 2010: Advances in Visual Computing pp 666-677 | Cite as
Modified Region Growing for Stereo of Slant and Textureless Surfaces
Conference paper
Abstract
In this paper, we present an algorithm for estimating disparity for images containing large textureless regions. We propose a fast and efficient region growing algorithm for estimating the stereo disparity. Though we present results on ice images, the algorithm can be easily used for other applications. We modify the first-best region growing algorithm using relaxed uniqueness constraints and matching for sub-pixel values and slant surfaces. We provide an efficient method for matching multiple windows using a linear transform. We estimate the parameters required by the algorithm automatically based on initial correspondences. Our method was tested on synthetic, benchmark and real outdoor data. We quantitatively demonstrated that our method performs well in all three cases.
Keywords
Epipolar Constraint Stereo Algorithm Disparity Range Region Growth Algorithm Stereo Disparity
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References
- 1.Akbarzadeh, A., Frahm, J.-M., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Merrell, P., Phelps, M., Sinha, S., Talton, B., Wang, L., Yang, Q., Stewenius, H., Yang, R., Welch, G., Towles, H., Nister, D., Pollefeys, M.: Towards urban 3d reconstruction from video. In: 3DPVT 2006: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT 2006), Washington, DC, USA, pp. 1–8. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
- 2.Murray, D., Little, J.: Using real-time stereo vision for mobile robot navigation. In: Autonomous Robots 2000 (2000)Google Scholar
- 3.Sumi, Y., Tomita, F.: 3d object recognition using segment-based stereo vision. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352, pp. 249–256. Springer, Heidelberg (1997)Google Scholar
- 4.Worby, A.P., Massom, R., Allison, I., Lytle, V.I., Heil, P.: East antarctic sea ice: a review of its structure, properties and drift. Antarctic Sea Ice Properties, Processes and Variability. AGU Antarctic Research Series, pp. 41–67 (1998)Google Scholar
- 5.Worby, A.P., Geiger, C., Paget, M., Woert, M.V., Ackley, S., DeLiberty, T.: The thickness distribution of antarctic sea ice. J. Geophys. Res., 158–170 (2000)Google Scholar
- 6.Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)CrossRefMATHGoogle Scholar
- 7.Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 418–433 (2005)CrossRefGoogle Scholar
- 8.Otto, G.P., Chau, T.K.W.: ‘region-growing’ algorithm for matching of terrain images. Image Vision Comput. 7, 83–94 (1989)CrossRefGoogle Scholar
- 9.Shin, D., Muller, J.-P.: An explicit growth model of the stereo region growing algorithm for parallel processing. ISPRS Commission V: Close Range Image Measurement Techniques (2010)Google Scholar
- 10.Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)Google Scholar
- 11.Čech, J., Šára, R.: Efficient sampling of disparity space for fast and accurate matching. In: CVPR Workshop Towards Benchmarking Automated Calibration, Orientation and Surface Reconstruction from Images, BenCOS 2007. IEEE, Los Alamitos (2007)Google Scholar
- 12.Ogale, A.S., Aloimonos, Y.: Shape and the stereo correspondence problem. Int. J. Comput. Vision 65, 147–162 (2005)CrossRefGoogle Scholar
- 13.Rohith, M.V., Somanath, G., Kambhamettu, C., Geiger, C.A.: Stereo analysis of low textured regions with application towards sea-ice reconstruction. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), pp. 23–29 (2009)Google Scholar
- 14.Rohith, M., Somanath, G., Kambhamettu, C., Geiger, C.: Towards estimation of dense disparities from stereo images containing large textureless regions. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–5 (2008)Google Scholar
- 15.Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 401–406 (1998)CrossRefGoogle Scholar
- 16.Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proceedings of Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 508–5152 (2001)Google Scholar
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