Abstract
Window selection is the main challenge for local stereo matching methods based on the rank transform and it involves two aspects : the rank window selection and the match window selection. Most recent methods only focus on how to select the match window but pay little attention to the selection of the rank window. In this paper, we propose a novel matching method based on adaptive rank transform. Differing with the existing rank-based matching methods, the proposed method can deal with the rank and match window selection at the same time. The experimental results are evaluated on the Middlebury dataset as well as real images, showing that our method performs better than the recent rank-based stereo matching methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Yang, Q.X., Ahuja, N.: A constant-space belief propagation algorithm for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1458–1465. IEEE Press, San Francisco (2010)
Cheng, L., Selzer, J.: Region-Tree based stereo using dynamic programming optimization. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 2378–2385. IEEE Press, New York (2006)
Srivastava, S., Seong, J.H.: Stereo matching using hierarchical belief propagation along ambiguity gradient. In: IEEE International Conference on Image Processing, vol. 36(2), pp. 2085–2088. IEEE Press, Cairo (2009)
Vanetti, M., Gallo, I., Elisabeta, B.: Dense two-frame stereo correspondence by self-orgnazing neural network. In: Internation Conference on Image Analysis and Processing, vol. 2, pp. 456–489 (2009)
Yoon, K.J., Kweon, I.J.: Adaptive support-weight approach for correspondence search. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(4), 650–656 (2006)
Guie, L., Yang, X.R., Qi, X.: Fast stereo matching algorithm using adaptive window. In: International Symposiums on Information Processing, pp. 25–30. IEEE Press, New York (2008)
Zhang, Z., Zhang, M.: Fast stereo matching algorithm based on adaptive window. In: International Conference on Audio Language and Image Processing, pp. 138–143 (2010)
Zabih, R., Woodfill, L.J.: Non-Parametric Local Transforms for Computing Visual Correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)
Banks, J., Bennamoun, M.: Reliability analysis of the rank transform for stereo matching. IEEE Transactions on Systems, Man, and Cybernetics 31(6), 870–880 (2001)
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)
Ambrosch, K., Humenberger, M.: Extending two non-parametric transforms for FPGA based stereo matching using bayer filtered cameras. In: Computer Vision and Pattern Recognition Workshops, vol. 1, pp. 1–8. IEEE Press, Anchorage (2008)
Wang, K.: Adaptive stereo matching algorithm based on edge detection. In: IEEE International Conference on Image Processing, vol. 1, pp. 1345–1348. IEEE Press, Singapore (2004)
Zheng, G., Su, X.Y., Liu, Y.K.: Local stereo matching with adaptive support weight, rank transform and diaparity calibration. Pattern Recognition Letters 29(1), 1230–1235 (2008)
Veksler, O.: Stereo Matching by compact windows via minimum radio cycle. In: International Conference on Computer Vision, vol. 1, pp. 540–547 (2001)
Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page (2008), http://vision.middlebury.edu/stereo/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, G., Du, Y., Tang, Y. (2011). Adaptive Rank Transform for Stereo Matching. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25489-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-25489-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25488-8
Online ISBN: 978-3-642-25489-5
eBook Packages: Computer ScienceComputer Science (R0)