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Fattening Free Block Matching

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Abstract

Block matching along epipolar lines is the core of most stereovision algorithms in geographic information systems. The usual distances between blocks are the sum of squared distances in the block (SSD) or the correlation. Minimizing these distances causes the fattening effect, by which the center of the block inherits the disparity of the more contrasted pixels in the block. This fattening error occurs everywhere in the image, and not just on strong depth discontinuities. The fattening effect at strong depth edges is a particular case of fattening, called foreground fattening effect. A theorem proved in the present paper shows that a simple and universal adaptive weighting of the SSD resolves the fattening problem at all smooth disparity points (a Spanish patent has been applied for by Universitat de Illes Balears (Reference P25155ES00, UIB, 2009)). The optimal SSD weights are nothing but the inverses of the squares of the image gradients in the epipolar direction. With these adaptive weights, it is shown that the optimal disparity function is the result of the convolution of the real disparity with a prefixed kernel. Experiments on simulated and real pairs prove that the method does what the theorem predicts, eliminating surface bumps caused by fattening. However, the method does not resolve the foreground fattening.

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References

  1. Buades, A., Coll, B., Morel, J.M., Rouge, B.: Procedimiento de establecimiento de correspondencia entre una primera imagen digital y una segunda imagen digital de una misma escena para la obtencion de disparidades. Spanish Patent, Reference P25155ES00, UIB (2009)

  2. Cao, F.: A Theory of Shape Identification. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  3. Cao, F., Delon, J., Desolneux, A., Muse, P., Sur, F.: A unified framework for detecting groups and application to shape recognition. J. Math. Imaging Vis. 27(2), 91–119 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Delon, J., Rougé, B.: Small baseline stereovision. J. Math. Imaging Vis. 28(3), 209–223 (2007)

    Article  Google Scholar 

  5. Fusiello, A., Roberto, V., Trucco, E.: Symmetric stereo with multiple windowing. Int. J. Pattern Recognit. Artif. Intell. 14(8), 1053–1066 (2000)

    Article  Google Scholar 

  6. Hirschmuller, H., Innocent, P.R., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. Int. J. Comput. Vis. 47(1-3), 229–246 (2002)

    Article  Google Scholar 

  7. Kanade, T., Okutomi, M.: A stereo matching algorithm with an adaptive window: Theory and experiment. IEEE Trans. Pattern Anal. Mach. Intell. 16(9), 920–932 (1994)

    Article  Google Scholar 

  8. Kimmel, R., Zhang, C., Bronstein, A.M., Bronstein, M.M.: Are mser features really interesting? IEEE Pami. In press (2010)

  9. Lotti, J., Giraudon, G.: Correlation algorithm with adaptive window for aerial image in stereo vision. In: Image and Signal Processing for Remote Sensing, vol. 2315, pp. 76–87 (1994)

    Google Scholar 

  10. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  11. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  12. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1), 43–72 (2005)

    Article  Google Scholar 

  13. Morel, J.M., Yu, G.: ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Musé, P., Sur, F., Cao, F., Gousseau, Y., Morel, J.-M.: An a contrario decision method for shape element recognition. Int. J. Comput. Vis. 69(3), 295–315 (2006)

    Article  Google Scholar 

  15. Patricio, M.P., Cabestaing, F., Colot, O., Bonnet, P.: A similarity-based adaptive neighborhood method for correlation-based stereo matching. In: International Conference on Image Processing, vol. 2, pp. 1341–1344 (2004)

    Google Scholar 

  16. Robert, L., Faugeras, O.D.: Curve-based stereo: figural continuity and curvature. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1991)

    Google Scholar 

  17. Sabater, N.: Reliability and accuracy in stereovision. Application to aerial and satellite high resolution images. Ph.D. thesis, ENS Cachan, December (2009)

  18. Sabater, N., Blanchet, G., Moisan, L., Almansa, A., Morel, J.-M.: Review of low-baseline stereo algorithms and benchmarks. In: Image and Signal Processing for Remote Sensing XVI, vol. 7830 (2010)

    Google Scholar 

  19. Scharstein, D., Szeliski, R.: Middlebury stereo vision page. Online at http://www.middlebury.edu/stereo (2002)

  20. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1/2/3), 7–42 (2002)

    Article  MATH  Google Scholar 

  21. Schmid, C., Zisserman, A.: The geometry and matching of lines and curves over multiple views. Int. J. Comput. Vis. 40(3), 199–234 (2000)

    Article  MATH  Google Scholar 

  22. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, vol. 846 (1998). CiteSeer

    Google Scholar 

  23. Veksler, O.: Fast variable window for stereo correspondence using integral images. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 556–561 (2003)

    Google Scholar 

  24. Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 798–805 (2006)

    Chapter  Google Scholar 

  25. Yaroslavsky, L., Eden, M.: Fundamentals of digital optics (2003)

  26. Yoon, K.-J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 650–656 (2006)

    Article  Google Scholar 

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Correspondence to A. Buades.

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This work was partially financed by spanish government MCYIT grant number TIN2008-04752.

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Blanchet, G., Buades, A., Coll, B. et al. Fattening Free Block Matching. J Math Imaging Vis 41, 109–121 (2011). https://doi.org/10.1007/s10851-011-0268-0

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