Advertisement

Wavelet-Based Correlation for Stereopsis

  • Maureen Clerc
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

Position disparity between two stereoscopic images, combined with camera calibration information, allow depth recovery. The measurement of position disparity is known to be ambiguous when the scene reflectance displays repetitive patterns. This problem is reduced if one analyzes scale disparity, as in shape from texture, which relies on the deformations of repetitive patterns to recover scene geometry from a single view.

These observations lead us to introduce a new correlation measure based not only on position disparity, but on position and scale disparity. Local scale disparity is expressed as a change in the scale of wavelet coefficients. Our work is related to the spatial frequency disparity analysis of Jones and Malik (ECCV92). We introduce a new wavelet-based correlation measure, and we show its application to stereopsis. We demonstrate its ability to reproduce perceptual results which no other method of our knowledge had accounted for.

Keywords

Correlation Measure Stereo Pair Gabor Wavelet Stereoscopic Image Stereo Image Pair 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Clerc, M. and Mallat, S. (2000) Estimating Deformations of Stationary Processes. Research Report no. 192, CERMICS, ENPC.Google Scholar
  2. 2.
    Devernay, F. and Faugeras, O. (1994) Computing Differential Properties of 3D Shapes from Stereoscopic Images without 3D Models. Research Report No. 2304, INRIA, July 1994.Google Scholar
  3. 3.
    Gårding, J. (1992). Shape from Texture for Smooth Surfaces under Perspective Projection. Journal of Mathematical Imaging and Vision 2, pp. 327–350.zbMATHCrossRefGoogle Scholar
  4. 4.
    Jones, D.G. and Malik, J. (1992). A Computational Framework for Determining Stereo Correspondence from a Set of Linear Spatial Filters. Proc. ECCV’92, pp. 395–410.Google Scholar
  5. 5.
    Jones, D.G. and Malik, J. (1992). Determining Three-Dimensional Shape from Orientation and Spatial Frequency Disparities. Proc. ECCV’92, pp. 661–669.Google Scholar
  6. 6.
    Malik, J. and Rosenholtz, R. (1997). Computing Local Surface Orientation and Shape From Texture for Curved Surfaces. Int. J. of Computer Vision 23–2, pp. 149–168.CrossRefGoogle Scholar
  7. 7.
    Mallat, S. (1997). A wavelet tour of signal processing. Academic Press.Google Scholar
  8. 8.
    Manmatha, R. (1994). Measuring the Affine Transform using Gaussian Filters. Proc. 3rd European Conf. on Computer Vision, pp. 159–164, Stockholm, Sweden.Google Scholar
  9. 9.
    Pan, H.P. (1996). General Stereo Image Matching using Symmetric Complex Wavelets. Proceedings of SPIE Wavelet Applications in Signal and Image Processing IV.Google Scholar
  10. 10.
    Perrin, J., Torrésani, and Fuchs, P. (1999). A Localized Correlation Function for Stereoscopic Image Matching. Traitement du Signal 16–1.Google Scholar
  11. 11.
    Schaffalitzky, F., Zisserman, A. (2001). Viewpoint invariant Texture Matching and Wide Baseline Stereo. Proceedings of ICCV.Google Scholar
  12. 12.
    Tyler, C.W. and Sutter, E.E. (1979). Depth from spatial frequency difference: an old kind of stereopsis? Vision Research 19:859–865.CrossRefGoogle Scholar
  13. 13.
    Zitnick, C.L. and Kanade, T. (2000). A Cooperative Algorithm for Stereo Matching and Occlusion Detection. IEEE Trans. Pat. Anal. and Mach. Intell. 22–7, pp. 675–684.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Maureen Clerc
    • 1
  1. 1.CERMICS, INRIASophia-AntipolisFrance

Personalised recommendations