Abstract
Existed local stereo methods usually choose the support region for aggregation using correlation or color information independently. The correlation cue works well with high textures but has a poor performance near depth discontinuities while the color cue plays the complementary role. In this paper we first propose a new soft segmentation approach for correlation-based aggregation. Then we make a combination of the two cues and adopt the advantages of them to overcome the limitation of each other. Our approach performs a two stage aggregation based on correlation and color respectively. Each stage is operated by a bilateral filter on the cost volume. The combination is simple and effective, which enables our approach to achieve a better performance in both highly textured areas and depth discontinuities than existed methods. The experimental results conform to our expectation and do make improvements to state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1), 7–42 (2002)
Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 15–18. IEEE (2006)
Yang, Q., Wang, L., Yang, R., Stewénius, H., Nistér, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(3), 492–504 (2009)
Wang, Z.F., Zheng, Z.G.: A region based stereo matching algorithm using cooperative optimization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Scharstein, D., Szeliski, R.: Middlebury stereo vision page, http://vision.middlebury.edu/stereo
Hirschmüller, H., Innocent, P., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. International Journal of Computer Vision 47(1), 229–246 (2002)
Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p–556. IEEE (2003)
Yoon, K., Kweon, I.: Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 650–656 (2006)
Tombari, F., Mattoccia, S., Di Stefano, L.: Segmentation-based adaptive support for accurate stereo correspondence. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 427–438. Springer, Heidelberg (2007)
Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local stereo matching using geodesic support weights. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2093–2096. IEEE (2009)
Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3017–3024. IEEE (2011)
Yang, Q., Yang, R., Davis, J., Nistér, D.: Spatial-depth super resolution for range images. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(4), 401–406 (1998)
Heo, Y.S., Lee, K.M., Lee, S.U.: Robust stereo matching using adaptive normalized cross-correlation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(4), 807–822 (2011)
Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(9), 1582–1599 (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998)
Cohen-Steiner, D., Alliez, P., Desbrun, M.: Variational shape approximation. ACM Transactions on Graphics (TOG) 23, 905–914 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Ju, R., Yang, Y., Xu, X., Xia, C., Wu, G. (2013). A Complementary Aggregation Approach for Local Stereo Matching Using Color and Correlation Cues. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-03731-8_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
eBook Packages: Computer ScienceComputer Science (R0)