FAST TEMPLATE MATCHING OF REPETITIVE OBJECTS IN STEREOSCOPY

Youval Nehmadi, Orly Kalantyrsky, Hugo Guterman

2012

Abstract

One of the challenges of stereovision is to process images with repetitive objects. In order to calculate the distance to an object, matching of the corresponding points between two images must be done. When repetitive objects exist, matching is not straightforward. Many known stereo methods rely on a uniqueness constraint. A uniqueness constraint assumes that only one correct match exists between stereo images. Some algorithms ignore repetitive objects and omit them in the depth map. We present a method that does not employ a uniqueness constraint, but rather determines whether an object is repetitive and then solves the matching problem by finding a unique object that is in close proximity to the object.

References

  1. Brown, M. Z., Burschka, D. and Hager, G. D. (2003). Advances in Computational Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 993-1008.
  2. Cyganek, B. and Siebert, J. P. (2009). An Introduction to 3D Computer Vision Techniques and Algorithms, Wiley.
  3. Fua, P. (1993). A Parallel Stereo Algorithm that Produces Dense Depth Maps and Preserves Image Features. Machine Vision and Applications, 6, 35-49.
  4. Hirschmüller, H. and Scharstein, D. (2007). Evaluation Of Cost Functions For Stereo Matching. IEEE Conference on Computer Vision and Pattern Recognition, 1-8.
  5. Gong, M. and Yang, Y. H. (2003). Fast Stereo Matching Using Reliability-Based Dynamic Programming and Consistency Constraints. Proceedings of the 9th IEEE International Conference on Computer Vision, 1, 610- 612.
  6. Kanade, T. and Okutomi, M. (1994). A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 920-932.
  7. Mühlmann, K., Maier, D., Hesser, J. and Männer, R. (2002). Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation. International Journal of Computer Vision, 47, 79-88.
  8. Okutomi, M. and Kanade, T. (1993). A Multiple-Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 353-363.
  9. Scharstein, D. and Pal, C. (2007). Learning Conditional Random Fields for Stereo. IEEE Conference on Computer Vision and Pattern Recognition.
  10. Scharstein, D. and Szeliski, R. (2002). A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision, 47, 7-42.
  11. Shechtman, E. and Irani, M. (2007). Matching Local SelfSimilarities across Images and Videos, IEEE Conference on Computer Vision and Pattern Recognition, 511-518.
  12. Szeliski, R. and Scharstein, D. (2002). Symmetric SubPixel Stereo Matching. Proceedings of the 7th European Conference on Computer Vision - Part II, 525-540.
  13. Zitova, B. and Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11), 977-1000.
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Paper Citation


in Harvard Style

Nehmadi Y., Kalantyrsky O. and Guterman H. (2012). FAST TEMPLATE MATCHING OF REPETITIVE OBJECTS IN STEREOSCOPY . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 198-205. DOI: 10.5220/0003778501980205


in Bibtex Style

@conference{icpram12,
author={Youval Nehmadi and Orly Kalantyrsky and Hugo Guterman},
title={FAST TEMPLATE MATCHING OF REPETITIVE OBJECTS IN STEREOSCOPY},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={198-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003778501980205},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - FAST TEMPLATE MATCHING OF REPETITIVE OBJECTS IN STEREOSCOPY
SN - 978-989-8425-99-7
AU - Nehmadi Y.
AU - Kalantyrsky O.
AU - Guterman H.
PY - 2012
SP - 198
EP - 205
DO - 10.5220/0003778501980205