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The Use of Color Information in Stereo Vision Processing

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High-Quality Visual Experience

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Binocular stereovision is the process of recovering the depth information of a visual scene, which makes it attractive for many applications like 3-D reconstruction, multiview video coding, safe navigation, 3-D television and free-viewpoint applications. The stereo correspondence problem, which is to identify the corresponding points in two or more images of the same scene, is the most important and difficult issue of stereo vision. In the literature, most of the stereo matching methods have been limited to gray level images. One information that has been largely neglected in computational stereo algorithms, although typically available in the stereo images, is color information. Color provides much more distinguishable information than intensity values and can therefore be used to significantly reduce the ambiguity between potential matches, while increasing the accuracy of the resulting matches. This would largely profit to stereo and multiview video coding, since efficient coding schemes exploit the cross-view redundancies based on a disparity estimation/compensation process. This chapter investigates the role of color information in solving the stereo correspondence problem. We test and compare different color spaces in order to evaluate their efficiency and suitability for stereo matching.

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References

  1. Fehn, C., Cooke, E., Schreer, O., Kauff, P.: 3D Analysis and Image-based Rendering for Immersive TV applications. Signal Processing: Image Communication 17(9), 705–715 (2002)

    Article  Google Scholar 

  2. Carranza, J., Theobalt, C., Magnor, M.A., Seidel, H.P.: Free-Viewpoint Video of Human Actors. ACM Transactions on Graphics, 569–577 (2003)

    Google Scholar 

  3. Chen, Y., Wang, Y.K., Ugur, K., Hannuksela, M., Lainema, J., Gabbouj, M.: The Emerging MVC Standard for 3D Video Services. EURASIP Journal on Advances in Signal Processing, Article ID 786015 (2009)

    Google Scholar 

  4. Scharstein, D., Szeliski, R.: A Taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Journal of Computer vision 47, 7–42 (2002)

    Article  MATH  Google Scholar 

  5. Koschan, A.: Dense Stereo Correspondence Using Polychromatic Block Matching. In: Chetverikov, D., Kropatsch, W.G. (eds.) CAIP 1993. LNCS, vol. 719, pp. 538–542. Springer, Heidelberg (1993)

    Google Scholar 

  6. Chambon, S., Crouzil, A.: Color stereo matching using correlation measures. In: Complex Systems Intelligence and Modern Technological Applications, Cherbourg, France, pp. 520–525 (2004)

    Google Scholar 

  7. Bleyer, M., Chambon, S., Poppe, U., Gelautz, M.: Evaluation of different methods for using colour information in global stereo matching approaches. In: The Congress of the International Society for Photogrammetry and Remote Sensing, Beijing, Chine (July 2008)

    Google Scholar 

  8. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  9. Fusiello, A., Roberto, V., Trucco, E.: A compact algorithm for rectification of stereo pairs. Machine Vision and Applications 12(1), 16–22 (2000)

    Article  Google Scholar 

  10. Kanade, T., Okutomi, M.: A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(9), 920–932 (1994)

    Article  Google Scholar 

  11. Veksler, O.: Stereo matching by compact windows via minimum ratio cycle. In: Proc. Int. Conf. on Computer Vision, Vancouver, BC, Canada, July 2001, vol. 1, pp. 540–547 (2001)

    Google Scholar 

  12. Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: Proc. Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii, vol. 1, pp. 156–161 (2002)

    Google Scholar 

  13. Fusiello, A., Roberto, V., Trucco, E.: Symmetric stereo with multiple windowing. Int. Journal of Pattern Recognition and Artificial Intelligence 14(8), 1053–1066 (2000)

    Article  Google Scholar 

  14. Wei, Y., Quan, L.: Region-based Progressive Stereo Matching. In: Proc. Computer Vision and Pattern Recognition, Washington, US, June 2004, vol. 1, pp. 106–113 (2004)

    Google Scholar 

  15. Kim, C., Lee, K.M., Choi, B.T., Lee, S.U.: A Dense Stereo Matching Using Two-Pass Dynamic Programming with Generalized Ground Control Points. In: Proc. Computer Vision and Pattern Recognition, San Diego, US, June 2005, vol. 2, pp. 1075–1082 (2005)

    Google Scholar 

  16. Bleyer, M., Gelautz, M.: A layered stereo algorithm using image segmentation and global visibility constraints. In: Proc. Int. Conf. on Image Processing, Singapour, October 2004, vol. 5, pp. 2997–3000 (2004)

    Google Scholar 

  17. Zhang, Y., Kambhamettu, C.: Stereo Matching with Segmentation-based Cooperation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 556–571. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Bleyer, M., Gelautz, M.: Graph-based surface reconstruction from stereo pairs using image segmentation. In: Videometrics VIII, San José, US, January 2005, vol. SPIE–5665, pp. 288–299 (2005)

    Google Scholar 

  19. Tappen, M.F., Freeman, W.T.: Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters. In: Proc. Int. Conf. Computer Vision, Nice, France, October 2003, vol. 2, pp. 900–907 (2003)

    Google Scholar 

  20. Alvarez, L., Deriche, R., Sanchez, J., Weickert, J.: Dense disparity map estimation respecting image discontinuities: A PDE and scale-space based approach. Journal of Visual Communication and Image Representation 13, 3–21 (2002)

    Article  Google Scholar 

  21. Slesareva, N., Bruhn, A., Weickert, J.: Optic flow goes stereo: A variational method for estimating discontinuity preserving dense disparity maps. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 33–40. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  22. Miled, W., Pesquet, J.-C., Parent, M.: A Convex Optimization Approach for Depth Estimation Under Illumination Variation. IEEE Transactions on Image Processing 18(4), 813–830 (2009)

    Article  MathSciNet  Google Scholar 

  23. Vandenbroucke, N., Macaire, L., Postaire, J.-G.: Color systems coding for color image processing. In: Int. Conf. Color in Graphics and Image Processing, Saint Etienne, France, October 2000, pp. 180–185 (2000)

    Google Scholar 

  24. Cie 15.2. Colorimetry, 2nd edn. Technical report, Commission Internationale de l’éclairage, Vienne, Autriche (1986)

    Google Scholar 

  25. Sharma, G., Trussel, H.J.: Digital color imaging. IEEE Transactions on Image Processing 6(7), 901–932 (1997)

    Article  Google Scholar 

  26. Richardson, I.: H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Wiley, Chichester (2003)

    Book  Google Scholar 

  27. Smith, A.R.: Color gamut transform pairs. Computer Graphics 12(3), 12–19 (1978)

    Article  Google Scholar 

  28. Ohta, Y.I., Kanade, T., Sakai, T.: Color Information for Region Segmentation. Computer Graphics and Image Processing 3(3), 222–241 (1980)

    Article  Google Scholar 

  29. Den Ouden, H.E.M., van Ee, R., de Haan, E.H.F.: Colour helps to solve the binocular matching problem. The Journal of Physiology 567(2), 665–671 (2005)

    Article  Google Scholar 

  30. Mühlmann, K., Maier, D., Hesser, J., Männer, R.: Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation. In: IEEE Workshop Stereo and Multi-Baseline Vision, Kauai, US, June 2001, pp. 30–36 (2001)

    Google Scholar 

  31. Koschan, A., Rodehorst, V., Spiller, K.: Color stereo vision using hierarchical block matching and active color illumination. In: Proc. Int. Conf. Pattern Recognition, Vienna, Austria, August 1996, vol. 1, pp. 835–839 (1996)

    Google Scholar 

  32. El Ansari, M., Masmoudi, L., Bensrhair, A.: A new regions matching for color stereo images. Pattern Recognit. Letters 28(13), 1679–1687 (2007)

    Article  Google Scholar 

  33. Belli, T., Cord, M., Philipp-Foliguet, S.: Colour contribution for stereo image matching. In: Proc. Int. Conf. Color in Graphics and Image Processing, Saint-Etienne, France, October 2000, pp. 317–322 (2000)

    Google Scholar 

  34. Jordan, J.R., Bovik, A.C.: Computational stereo vision using color. IEEE Control Systems Magazine 8(3), 31–36 (1988)

    Article  MATH  Google Scholar 

  35. Chambon, S., Crouzil, A.: Colour correlation-based matching. International Journal of Robotics and Automation 20(2), 78–85 (2005)

    Article  Google Scholar 

  36. Alvarez, L., Sánchez, J.: 3-D geometry reconstruction using a color image stereo pair and partial differential equations. Cuadernos del Instituto Universitario de Ciencias y Tecnologas Cibernticase, Las Palmas. Technical report (2000)

    Google Scholar 

  37. Miled, W., Pesquet-Popescu, B., Pesquet, J.-C.: A Convex Programming Approach for Color Stereo Matching. In: Inter. Workshop on Multimedia Signal Processing, Queensland, Australie (2008)

    Google Scholar 

  38. Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, vol. 1, pp. 74–81 (2004)

    Google Scholar 

  39. Wei, Y., Quan, L.: Region-based progressive stereo matching. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, vol. 1, pp. 106–113 (2004)

    Google Scholar 

  40. Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Int. Conf. Computer Vision, Vancouver, Canada, vol. 2, pp. 508–515 (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. TSI Department, TELECOM ParisTech, 46 rue Barrault, 75634, Paris Cédex 13, France

    Wided Miled & Béatrice Pesquet-Popescu

Authors
  1. Wided Miled
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  2. Béatrice Pesquet-Popescu
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Editor information

Editors and Affiliations

  1. University of Surrey, Guildford, UK

    Marta Mrak

  2. University of Zagreb, Zagreb, Croatia

    Mislav Grgic

  3. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Murat Kunt

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Miled, W., Pesquet-Popescu, B. (2010). The Use of Color Information in Stereo Vision Processing. In: Mrak, M., Grgic, M., Kunt, M. (eds) High-Quality Visual Experience. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12802-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-12802-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12801-1

  • Online ISBN: 978-3-642-12802-8

  • eBook Packages: EngineeringEngineering (R0)

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