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International Journal of Computer Vision

, Volume 72, Issue 1, pp 9–25 | Cite as

A Roadmap to the Integration of Early Visual Modules

  • Abhijit S. Ogale
  • Yiannis Aloimonos
Article

Abstract

By examining the problem of image correspondence (binocular stereo and optical flow) and its relationship with other modules such as segmentation, shape and depth estimation, occlusion detection, and local signal processing, we argue that early visual modules are entangled in chicken-and-egg relationships, and unraveling these necessitates a compositional approach. In this paper, we present compositional algorithms which can match images containing slanted surfaces and images having different contrast, while simultaneously solving other problems as part of the same process. Ultimately, our goal is to motivate the application of the compositional approach to unify many other early visual modules. Experimental results have been presented on a large variety of stereo and motion images, including images with contrast mismatch and images containing untextured slanted surfaces.

Keywords

Uniqueness Constraint Stereo Match Stereo Pair Binocular Disparity Local Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aloimonos, Y. and Shulman, D. 1989. Integration of Visual Modules: An Extension of the Marr Paradigm. Academic Press.Google Scholar
  2. Alvarez, L., Deriche, R., Papadapoulo, T. and Sanchez, J. 2002. Symmetrical dense optical flow estimation with occlusion detection. European Conference on Computer Vision, I:721ff.Google Scholar
  3. Barnard, S. T. 1989. Stochastic stereo matching over scale. International Journal of Computer Vision, 3(1):17–32.MathSciNetCrossRefGoogle Scholar
  4. Barron, J., Fleet, D., Beauchemin, S. and Burkitt, T. 1994. Performance of optical flow techniques. IEEE Conference on Computer Vision and Pattern Recognition, (92):236–242.Google Scholar
  5. Beauchemin, S.S. and Barron, J.L. 1995. The computation of optical flow. ACM Computing Surveys, 27(3):433–467.CrossRefGoogle Scholar
  6. Beauchemin, S. and Barron, J. 2000. On the fourier properties of discontinuous visual motion. Journal of Mathematical Imaging and Vision, 13(3):155–172.zbMATHMathSciNetCrossRefGoogle Scholar
  7. Birchfield, S. and Tomasi, C. 1998. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(4):401–406.CrossRefGoogle Scholar
  8. Black, M. and Fleet, D. 2000. Probabilistic detection and tracking of motion discontinuities. International Journal of Computer Vision, 38(3):231–245.zbMATHCrossRefGoogle Scholar
  9. Bobick, A.F. and Intille, S.S. 1999. Large occlusion stereo. International Journal of Computer Vision, 33(3):181–200.CrossRefGoogle Scholar
  10. Boykov, Y., Veksler, O. and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(11):1222–1239.CrossRefGoogle Scholar
  11. Boykov, Y., Veksler, O. and Zabih, R. 1998. A variable window approach to early vision. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(12):1283–1294.CrossRefGoogle Scholar
  12. Egnal, G. and Wildes, R. 2002. Detecting binocular half-occlusions: empirical comparisons of five approaches. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8):1127–1133.CrossRefGoogle Scholar
  13. Fleet, D. 1994. Disparity from local weighted phase-correlation. IEEE International Conference on SMC, 48–56.Google Scholar
  14. Fleet, D., Jepson, A. and Jenkin, M. 1991. Phase-based disparity measurement. CVGIP: Image Understanding, (53):198–210.Google Scholar
  15. Fusiello, A., Roberto, V. and Trucco, E. 1997. Efficient stereo with multiple windowing. IEEE Conference on Computer Vision and Pattern Recognition, 858–863.Google Scholar
  16. Galvin, B., McCane, B., Novins, K., Mason, D. and Mills, S. 1998. Recovering motion fields: An evaluation of eight optical flow algorithms. Proceedings of the British Machine Vision Converence.Google Scholar
  17. Geiger, D., Ladendorf, B. and Yuille, A. 1992. Occlusions and binocular stereo. European Conference on Computer Vision, 425–433.Google Scholar
  18. Geman, S. and Geman, D. 1984. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(6): 721–741.zbMATHGoogle Scholar
  19. Jenkin, M. and Jepson, A. 1988. Computational processes in Human Vision. (ed.) Z. Pylyshn, Ablex Press, NJ, ch. The measurement of binocular disparity.Google Scholar
  20. Julesz, B. 1971. Foundations of Cyclopean Perception. University of Chicago Press, Chicago.Google Scholar
  21. Kanade, T. and Okutomi, M. 1994. A stereo matching algorithm with an adaptive window: theory and experiment. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(9):920–932.CrossRefGoogle Scholar
  22. Kim, J., Kolmogorov, V. and Zabih, R. 2003. Visual correspondence using energy minimization and mutual information. International Conference on Computer Vision, (2):1033–1040.Google Scholar
  23. Kolmogorov, V. and Zabih, R. 2001. Computing visual correspondence with occlusions using graph cuts. International Conference on Computer Vision, 508–515.Google Scholar
  24. Liu, H., Hong, T., Herman, M., Camus, T. and Chellappa, R. 1998. Accuracy vs efficiency trade-offs in optical flow algorithms. Computer Vision and Image Understanding, 72(3):271–286.CrossRefGoogle Scholar
  25. Mitiche, A. and Bouthemy, P. 1996. Computation and analysis of image motion: a synopsis of current problems and methods. International Journal of Computer Vision, 19(1):29–55.CrossRefGoogle Scholar
  26. Nestares, O., Navarro, R., Portilla, J. and Tabernero, A. 1998. Efficient spatial-domain implementation of a multiscale image representation based on gabor functions. J. Electronic Imaging, (7):166–173.Google Scholar
  27. Ogale, A. and Aloimonos, Y. 2004. Stereo correspondence with slanted surfaces: critical implications of horizontal slant. IEEE Conference on Computer Vision and Pattern Recognition, (1):568–573.Google Scholar
  28. Ogale, A. and Aloimonos, Y. 2005. Shape and the stereo correspondence problem. International Journal of Computer Vision, 65(3):147–162.CrossRefGoogle Scholar
  29. Ogale, A., Fermuller, C. and Aloimonos, Y. 2005. Motion segmentation using occlusions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6):988–992.CrossRefGoogle Scholar
  30. Ohta, Y. and Kanade, T. 1985. Stereo by intra- and inter-scanline search using dynamic programming. IEEE Trans. on Pattern Analysis and Machine Intelligence, 7(2):139–154.CrossRefGoogle Scholar
  31. Okutomi, M. and Kanade, T. 1993. A multiple baseline stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4):353–363.CrossRefGoogle Scholar
  32. Qian, N. 1994. Computing stereo disparity and motion with known binocular cell properties. Neural Computation, (6):390–404.Google Scholar
  33. Roy, S. and Cox, I. 1998. A maximum-flow formulation of the n-camera stereo correspondence problem. International Conference on Computer Vision, 492–499.Google Scholar
  34. Sanger, T. 1988. Stereo disparity computation using gabor filters. Biological Cybernetics, (59):405–418.Google Scholar
  35. Scharstein, D. and Szeliski, R. 1998. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155–174.CrossRefGoogle Scholar
  36. Scharstein, D. and Szeliski, R. 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1):7–42.zbMATHCrossRefGoogle Scholar
  37. Szeliski, R. 1990. Bayesian modeling of uncertainty in low-level vision. International Journal of Computer Vision, 5(3):271–302.CrossRefGoogle Scholar
  38. Tao, H., Sawhney, H. and Kumar, R. 2001. A global matching framework for stereo computation. International Conference on Computer Vision, (1):532–539.Google Scholar
  39. Weng, J. 1994. Image matching using windowed fourier phase. International Journal of Computer Vision, (11):211–236.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Center for Automation ResearchUniversity of MarylandCollege Park

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