A NEW LIKELIHOOD FUNCTION FOR STEREO MATCHING - How to Achieve Invariance to Unknown Texture, Gains and Offsets?

Ferdinand van der Heijden, Luuk J. Spreeuwers, Sanja Damjanovic

Abstract

We introduce a new likelihood function for window-based stereo matching. This likelihood can cope with unknown textures, uncertain gain factors, uncertain offsets, and correlated noise. The method can be fine-tuned to the uncertainty ranges of the gains and offsets, rather than a full, blunt normalization as in NCC (normalized cross correlation). The likelihood is based on a sound probabilistic model. As such it can be directly used within a probabilistic framework. We demonstrate this by embedding the likelihood in a HMM (hidden Markov model) formulation of the 3D reconstruction problem, and applying this to a test scene. We compare the reconstruction results with the results when the similarity measure is the NCC, and we show that our likelihood fits better within the probabilistic frame for stereo matching than NCC.

References

  1. Cox, I. J., Hingorani, S. L., Rao, S. B., and Maggs, B. M. (1996). A maximum likelihood stereo algorithm. Comput. Vis. Image Underst., 63(3):542-567.
  2. Egnal, G. (2000). mutual information as a stereo correspondence measure. Technical Report Technical Report MS-CIS-00-20, Comp. and Inf. Science, U. of Pennsylvania.
  3. Faugeras, O. (1993). Three-Dimensional Computer Vision - A Geometric Viewpoint. The MIT Press.
  4. Hirschmller, H. and Scharstein, D. (2007). Evaluation of cost functions for stereo matching. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007). http://vision.middlebury.edu/stereo/data/.
  5. Roy, S. and Cox, I. J. (1998). A maximum-flow formulation of the n-camera stereo correspondence problem. In ICCV 7898: Proceedings of the Sixth International Conference on Computer Vision, page 492, Washington, DC, USA. IEEE Computer Society.
  6. Spreeuwers, L. (2008). Multi-view passive acquisition device for 3d face recognition. In BIOSIG 2008: Biometrik und elektronische Signaturen.
  7. van der Heijden, F., Duin, R., de Ridder, D., and Tax, D. (2004). Classification, Parameter Estimation and State Estimation An Engineering Approach Using MATLAB. WILEY.
Download


Paper Citation


in Harvard Style

van der Heijden F., J. Spreeuwers L. and Damjanovic S. (2009). A NEW LIKELIHOOD FUNCTION FOR STEREO MATCHING - How to Achieve Invariance to Unknown Texture, Gains and Offsets? . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 603-608. DOI: 10.5220/0001793606030608


in Bibtex Style

@conference{visapp09,
author={Ferdinand van der Heijden and Luuk J. Spreeuwers and Sanja Damjanovic},
title={A NEW LIKELIHOOD FUNCTION FOR STEREO MATCHING - How to Achieve Invariance to Unknown Texture, Gains and Offsets?},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={603-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001793606030608},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - A NEW LIKELIHOOD FUNCTION FOR STEREO MATCHING - How to Achieve Invariance to Unknown Texture, Gains and Offsets?
SN - 978-989-8111-69-2
AU - van der Heijden F.
AU - J. Spreeuwers L.
AU - Damjanovic S.
PY - 2009
SP - 603
EP - 608
DO - 10.5220/0001793606030608