Genetic Algorithm for Stereo Correspondence with a Novel Fitness Function and Occlusion Handling

Alvaro Arranz, Alvaro Sanchez-Miralles, Jaime Boal, Manuel Alvar, Arturo de la Escalera

2013

Abstract

This paper proposes a genetic algorithm for solving the stereo correspondence problem. Applied to stereo, genetic algorithms are flexible in the cost function and permit global reasoning. The main contribution of this paper is a new crossover and a mutation operator which accounts for occlusion management and a new fitness function which considers occluded pixels and photometric derivatives. Both left and right disparity images are analysed in order to classify occluded pixels correctly. The proposed fitness function is compared to the traditional energy function based in the framework of the Markov Random Fields. The results show that a 32% bad-pixel error reduction can be achieved on average using the proposed fitness function. The results have been uploaded to the Middlebury ranking webpage, as the first evolutionary algorithm evaluated.

References

  1. Alahari, K., Kohli, P., and Torr, P. H. S. (2010). Dynamic hybrid algorithms for map inference in discrete mrfs. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(10):1846-1857.
  2. Boykov, Y., Veksler, O., and Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222-1239.
  3. Dai, C., Wu, X., and Liu, J. (2008). Stereo matching using adaptive genetic algorithm. In Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on, pages 1225-1228.
  4. Gong, M. and Yang, Y.-H. (2001). Multi-resolution stereo matching using genetic algorithm. In Stereo and Multi-Baseline Vision, 2001. (SMBV 2001). Proceedings. IEEE Workshop on, pages 21-29.
  5. Gong, M. and Yang, Y.-H. (2002). Genetic-based stereo algorithm and disparity map evaluation. International Journal of Computer Vision, 47(1):63-77.
  6. Han, K.-P., Song, K.-W., Chung, E.-Y., Cho, S.-J., and Ha, Y.-H. (2001). Stereo matching using genetic algorithm with adaptive chromosomes. Pattern Recognition, 34(9):1729-1740.
  7. Issa, H., Ruichek, Y., and Postaire, J. G. (2002). Stereo correspondence using a genetic scheme with a new solution encoding. In Systems, Man and Cybernetics, 2002 IEEE International Conference on, volume 6, page 5 pp. vol.6.
  8. Kolmogorov, V. and Zabin, R. (2004). What energy functions can be minimized via graph cuts? Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(2):147-159.
  9. Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., and Zhang, X. (2011). On building an accurate stereo matching system on graphics hardware. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 467 -474.
  10. Nie, D.-H., Han, K.-P., and Lee, H.-S. (2009). Stereo matching algorithm using population-based incremental learning on gpu. In Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, pages 1-4.
  11. Saito, H. and Mori, M. (1995). Application of genetic algorithms to stereo matching of images. Pattern Recognition Letters, 16(8):815-821.
  12. Wang, B., Chung, R., and Shen, C.-L. (2003). Genetic algorithm-based stereo vision with no blockpartitioning of input images. In Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on, volume 2, pages 830-836 vol.2.
  13. Yoon, K. J. and Kweon, I. S. (2006). Adaptive supportweight approach for correspondence search. Ieee Transactions On Pattern Analysis And Machine Intelligence, 28(4):650-656.
  14. Zhang, Z., Hou, C., and Yang, J. (2009). A stereo matching algorithm based on genetic algorithm with propagation stratagem. In Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, pages 1-4.
Download


Paper Citation


in Harvard Style

Arranz A., Sanchez-Miralles A., Boal J., Alvar M. and de la Escalera A. (2013). Genetic Algorithm for Stereo Correspondence with a Novel Fitness Function and Occlusion Handling . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 294-299. DOI: 10.5220/0004291202940299


in Bibtex Style

@conference{visapp13,
author={Alvaro Arranz and Alvaro Sanchez-Miralles and Jaime Boal and Manuel Alvar and Arturo de la Escalera},
title={Genetic Algorithm for Stereo Correspondence with a Novel Fitness Function and Occlusion Handling},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={294-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004291202940299},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Genetic Algorithm for Stereo Correspondence with a Novel Fitness Function and Occlusion Handling
SN - 978-989-8565-48-8
AU - Arranz A.
AU - Sanchez-Miralles A.
AU - Boal J.
AU - Alvar M.
AU - de la Escalera A.
PY - 2013
SP - 294
EP - 299
DO - 10.5220/0004291202940299