Abstract
To make a matching algorithm to satisfy the requirements of high precision and anti-interference, a novel stereo-matching algorithm with efficient matching cost and adaptive guided image filter is proposed. Firstly, we adopt a modified Census transform with a local texture metric to compute the initial cost. It can make full use of the cross-correlation information between pixels. Meanwhile, we incorporate the Census, color and gradient costs as a mixed matching cost algorithm. Then, we aggregate the costs with guided image filter based on adaptive rectangular support window instead of the traditional fixed support window. The variable kernel window is constructed by the local color similarity and spatial distance. In this way, less occluded points will be included in the support region. On this basis, we adopt integral image to further speed up the computation of this step. Finally, the initial disparity of each pixel is selected using winner takes all optimization and the final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm not only achieves an average error rate of 5.22 % on the Middlebury stereo benchmark data set, but can also overcome the influence of illumination distortion in the matching effectively.
Similar content being viewed by others
References
Scharsterein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 7–42 (2002)
Kim, J.K., Lee, K.M., Choi, B.T.: A dense stereo matching using two-pass dynamic programming with generalized ground control points. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. pp. 1075–1082, San Diego (2005)
Chang, X., Zhou, Z., Wang, L., Shi, Y., Zhao, Q.: Real-time accurate stereo matching using modified two-pass aggregation and winner-take-all guided dynamic programming. In: International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 73–79, Hangzhou (2011)
Bleyer, M., Gelautz, M.: Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions. Signal Process Image 22, 127–143 (2007)
Papadakis, N., Caselles, V.: Multi-label depth estimation for graph cuts stereo problems. J. Math. Imaging Vis. 38, 70–82 (2010)
Besse, F., Rother, C., Fitzgibbon, A.: PMBP: patch match belief propagation for correspondence field estimation. Int. J. Comput. Vis. 110, 2–13 (2013)
Yang, Q., Wang, L., Ahuja, N.: A constant-space belief propagation algorithm for stereo matching. In: Proceeding of IEEE Conference Computer Vision and Pattern Recognition, San Francisco (2010)
Stefano, L.D., Marchionni, M., Mattoccia, S.: A fast area-based stereo matching algorithm. Image Vis. Comput. 22, 983–1005 (2004)
Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006)
Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 807-814, San Diego (2005)
De-Maeztu, L., Villanueva, A., Cabeza, R.: Stereo matching using gradient similarity and locally adaptive support-weight. Pattern Recogn. Lett. 32, 1643–1651 (2011)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Computer Vision, ECCV’94, pp. 151–158. Springer, Berlin (1994)
Lee, Z., Juang, J., Nguyen, T.: Local disparity estimation with three-mode cross census and advanced support weight. IEEE Trans. Multimed. 15, 1855–1864 (2013)
Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1582–1599 (2009)
Sun, C.: A fast stereo matching method. In: Digital Image Computing: Techniques and Applications, pp. 95–100, Csiro (1997)
Fusiello, A., Roberto, V., Trucco, E.: Efficient stereo with multiple windowing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 858–863, San Juan (1997)
Kanade, T., Okutomi, M.: A stereo matching algorithm with an adaptive window: theory and experiment. IEEE Trans. Pattern Anal. Mach. Intell. 16, 920–932 (1994)
Boykov, Y., Veksler, O., Zabih, R.: A variable window approach to early vision. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1283–1294 (1998)
Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 556–561, Princeton (2003)
Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. In: GPUCV, Barcelona (2011)
Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local stereo matching using geodesic support weights. In: Proceedings of IEEE International Conference on Image Processing, pp. 2093–2096, Cario (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 839–846, Bombay (1998)
He, K., Shun, J., Tang, X.: Guided image filter. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1397–1409 (2013)
Hosni, A., Bleyer, M., Rhemann, C., Gelautz, M., Rother, C.: Real-time local stereo matching using guided image filtering. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1–6, Barcelona (2011)
De-Maeztu, L., Mattoccia, S., Villanueva, A., Cabeza, R.: Linear stereo matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1708–1715, Barcelona (2011)
Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuit Syst. Video Technol 19, 1073–1079 (2009)
Yang, Q., Ji, P., Li, D., Yao, S., Zhang, M.: Fast stereo matching using adaptive guided filtering. Image Vis. Comput. 32, 202–211 (2014)
Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page. http://vision.middlebury.edu/stereo/. Accessed 20 Dec 2011
Ambrosch, K., Kubinger, W.: Accurate hardware-based stereo vision. Comput. Vis. Image Underst. 114, 1303–1316 (2010)
Hosni, A., Rhemann, C., Bleyer, M.: Fast cost-volume filtering for visual correspondence and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 35, 504–511 (2013)
Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings of 3DPVT, pp. 798–805, Chapel Hill (2006)
Mattoccia, S., Giardino, S., Gambini, A.: Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering. In: Asian Conference on Computer Vision, pp. 371–380 (ACCV) (2009)
Tang, Y., Shi, X., Xiao, T., Fan, J.: An improved image analogy method based on adaptive CUDA-accelerated neighborhood matching framework. Vis. Comput. 28, 743–753 (2012)
Xu, Y., Zhao, Y., Ji, M.: Local stereo matching with adaptive shape support window based cost aggregation. Appl. Optics 53, 6885–6892 (2014)
Liu, J., Li, C., M, F., W, Z.: 3D entity-based stereo matching with ground control points and joint second-order smoothness prior. Vis. Comput. 31, 1253–1269 (2015)
Xu, Z., Ma, L., Kimachi, M., Suwa, M.: Efficient contrast invariants tereo correspondence using dynamic programming with vertical constraint. Vis. Comput. 24, 45–55 (2008)
Kurt, M., Ozturk, A., Peers, P.: A compact tucker-based factorization model for heterogeneous subsurface scattering. In: TPCG, pp. 85–92 (2013)
Acknowledgments
This work was funded by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61375025, 61075011, and 60675018, and also the Scientific Research Foundation for the Returned Overseas Chinese Scholars from the State Education Ministry of China.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhu, S., Yan, L. Local stereo matching algorithm with efficient matching cost and adaptive guided image filter. Vis Comput 33, 1087–1102 (2017). https://doi.org/10.1007/s00371-016-1264-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-016-1264-6