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Evolutionary Algorithm for Dense Pixel Matching in Presence of Distortions

  • Ana Carolina dos-Santos-Paulino
  • Jean-Christophe Nebel
  • Francisco Flórez-RevueltaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

Dense pixel matching is an essential step required by many computer vision applications. While a large body of work has addressed quite successfully the rectified scenario, accurate pixel correspondence between an image and a distorted version remains very challenging. Exploiting an analogy between sequences of genetic material and images, we propose a novel genetics inspired algorithm where image variability is treated as the product of a set of image mutations. As a consequence, correspondence for each scanline of the initial image is formulated as the optimisation of a path in the second image minimising a fitness function penalising mutations. This optimisation is performed by a evolutionary algorithm which, in addition to provide fast convergence, implicitly ensures consistency between successive scanlines. Performance evaluation on locally and globally distorted images validates our bio-inspired approach.

Keywords

Evolutionary algorithm Dense pixel matching Unrectified images Distorted images 

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References

  1. 1.
    Alba, E., Dorronsoro, B.: Cellular genetic algorithms. vol. 42. Springer (2008)Google Scholar
  2. 2.
    Cockshott, W.P., Hoff, S., Nebel, J.C.: Experimental 3-D digital TV studio. IEE Proceedings of the Vision, Image and Signal Processing 150(1), 28–33 (2003)CrossRefGoogle Scholar
  3. 3.
    Deng, Y., Lin, X.: A Fast Line Segment Based Dense Stereo Algorithm Using Tree Dynamic Programming. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 201–212. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Han, K.P., Song, K.W., Chung, E.Y., Cho, S.J., Ha, Y.H.: Stereo matching using genetic algorithm with adaptive chromosomes. Pattern Recognition 34(9), 1729–1740 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Hartley, R.I.: Theory and practice of projective rectification. International Journal of Computer Vision 35(2), 115–127 (1999)CrossRefGoogle Scholar
  6. 6.
    Khambay, B., Nebel, J.C., Bowman, J., Ayoub, A., Walker, F., Donald, H.D.: A pilot study: 3D stereo photogrammetric image superimposition on to 3D CT scan images - the future of orthognathic surgery. The International Journal of Adult Orthodontics and Orthognathic Surgery 17(4), 331–341 (2002)Google Scholar
  7. 7.
    Kiperwasser, E., David, O., Netanyahu, N.S.: A hybrid genetic approach for stereo matching. In: Proceeding of the 15th Genetic and Evolutionary Computation Conference, pp. 1325–1332. ACM, New York (2013)Google Scholar
  8. 8.
    Nalpantidis, L., Amanatiadis, A., Sirakoulis, G., Kyriakoulis, N., Gasteratos, A.: Dense disparity estimation using a hierarchical matching technique from uncalibrated stereo vision. In: IEEE International Workshop on Imaging Systems and Techniques, pp. 427–431 (2009)Google Scholar
  9. 9.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3), 443–453 (1970)CrossRefGoogle Scholar
  10. 10.
    Roy, S., Meunier, J., Cox, I.J.: Cylindrical rectification to minimize epipolar distortion. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 393–399 (1997)Google Scholar
  11. 11.
    Rzeszutek, R., Tian, D., Vetro, A.: Disparity estimation of misaligned images in a scanline optimization framework. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1523–1527 (2013)Google Scholar
  12. 12.
    Saito, H., Mori, M.: Application of genetic algorithms to stereo matching of images. Pattern Recognition Letters 16(8), 815–821 (1995)CrossRefGoogle Scholar
  13. 13.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Intl. Journal of Computer Vision 47(1–3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Thevenon, J., Martinez del Rincon, J., Dieny, R., Nebel, J.C.: Dense pixel matching between unrectified and distorted images using dynamic programming. In: Intl. Conference on Computer Vision Theory and Applications, pp. 216–224 (2012)Google Scholar
  15. 15.
    Tippetts, B., Lee, D., Lillywhite, K., Archibald, J.: Review of stereo vision algorithms and their suitability for resource-limited systems. Journal of Real-Time Image Processing, 1–21 (2013)Google Scholar
  16. 16.
    Veksler, O.: Stereo correspondence by dynamic programming on a tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 384–390 (2005)Google Scholar
  17. 17.
    Wan, D., Zhou, J.: Self-calibration of spherical rectification for a ptz-stereo system. Image and Vision Computing 28(3), 367–375 (2010)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Zhengping, J.: On the multi-scale iconic representation for low-level computer vision systems. PhD thesis, The Turing Institute and the U. of Strathclyde (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ana Carolina dos-Santos-Paulino
    • 1
  • Jean-Christophe Nebel
    • 2
  • Francisco Flórez-Revuelta
    • 2
    Email author
  1. 1.Télécom Physique StrasbourgUniversité de StrasbourgIllkirch-GraffenstadenFrance
  2. 2.Faculty of Science, Engineering and ComputingKingston UniversityKingston upon ThamesUK

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