Abstract
We propose a novel approach for estimating a depth-map from a pair of rectified stereo images degraded by blur and contrast change. At each location in image space, information is encoded with a new class of descriptors that are invariant to convolution with centrally symmetric PSF and to variations in contrast. The descriptors are based on local-phase quantization, they can be computed very efficiently and encoded in a limited number of bits. A simple measure for comparing two encoded templates is also introduced. Results show that, the proposed method can represent a cheap but still effective way for estimating disparity maps from degraded images, without making restrictive assumptions; these advantages make it attractive for practical applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA (2007)
Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA (2007)
Flusser, J., Suk, T.: Degraded image analysis: an invariant approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 590–603 (1998); CRAS Paris 287, 1013–1015 (1978)
van de Weijer, J., Schmid, C.: Blur robust and color constant image description. In: Proceedings of ICIP, Atlanta, USA (2006)
Ojansivu, V., Heikkilä, J.: Image registration using blur invariant phase correlation. IEEE Signal Processing Letters 14(7), 449–452 (2007)
Ogale, A.S., Aloimonos, Y.: Robust contrast invariant stereo correspondence. In: Proc. IEEE Conf. on Robotics and Automation, ICRA (2005)
Tsing, Y., Kang, S.B., Szelinski, R.: Stereo matching with linear superposition of layers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 290–301 (2006)
Rajagopalan, A.N., Mudenagudi, U.: Depth estimation and image restoration using defocused stereo pairs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1521–1525 (2004)
Frese, C., Gheta, I.: Robust depth estimation by fusion of stereo and focus series acquired with a camera array. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 234–248 (2006)
Wang, L., Gong, M., Yang, R.: How far can we go with local optimization in real-time stereo matching. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 129–136 (2006)
Curtis, S.R., Lim, J.S., Oppenheim, A.V.: Signal reconstruction from fourier transform sign information. Technical report 500, Massachusetts Institute of Technology. Research Laboratory of Electronics (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pedone, M., Heikkilä, J. (2008). Blur and Contrast Invariant Fast Stereo Matching. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-88458-3_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88457-6
Online ISBN: 978-3-540-88458-3
eBook Packages: Computer ScienceComputer Science (R0)