Computing Visual Correspondence with Occlusions via Graph Cuts
Computing Visual Correspondence with Occlusions via Graph Cuts Computing Visual Correspondence with Occlusions via Graph Cuts
References [1] Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. [2] P.N. Belhumeur and D. Mumford. A Bayesian treatment of the stereo correspondence problem using half-occluded regions. In IEEE Confer- ence on Computer Vision and Pattern Recognition, pages 506–512, 1992. Revised version appears in IJCV. [3] Stan Birchfield and Carlo Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4):401–406, April 1998. [4] A.F. Bobick and S.S. Intille. Large occlusion stereo. International Jour- nal of Computer Vision, 33(3):1–20, September 1999. [5] Robert C. Bolles and John Woodfill. Spatiotemporal consistency check- ing of passive range data. In International Symposium on Robotics Re- search, 1993. Pittsburg, PA. [6] Yuri Boykov and Vladimir Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in computer vision. In International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, volume 2134 of LNCS, pages 359–374. Springer-Verlag, September 2001. 30
[7] Yuri Boykov, Olga Veksler, and Ramin Zabih. Markov Random Fields with efficient approximations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 648–655, 1998. [8] Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222–1239, November 2001. [9] L. Ford and D. Fulkerson. Flows in Networks. Princeton University Press, 1962. [10] D. Geiger, B. Ladendorf, and A. Yuille. Occlusions and binocular stereo. International Journal of Computer Vision, 14(3):211–226, April 1995. [11] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984. [12] D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori estimation for binary images. JournaloftheRoyalStatisticalSociety, Series B, 51(2):271–279, 1989. [13] H. Ishikawa and D. Geiger. Occlusions, discontinuities, and epipolar lines in stereo. In European Conference on Computer Vision, pages 232–248, 1998. [14] H. Ishikawa and D. Geiger. Segmentation by grouping junctions. In IEEE Conference on Computer Vision and Pattern Recognition, pages 125–131, 1998. 31
- Page 1 and 2: Computing Visual Correspondence wit
- Page 3 and 4: The new algorithms proposed in this
- Page 5 and 6: in the right image. Furthermore, th
- Page 7 and 8: • a smoothness term Esmooth, whic
- Page 9 and 10: not permit occlusions to be natural
- Page 11 and 12: The most closely related work consi
- Page 13 and 14: Figure 3: The graph corresponding t
- Page 15 and 16: • the last edge that we add is (v
- Page 17 and 18: 2. Occlusion term. ˜Eocc(x) = Cp
- Page 19 and 20: The corresponding graph is shown in
- Page 21 and 22: First image Second image Horizontal
- Page 23 and 24: Left image Right image Horizontal m
- Page 25 and 26: distance. Occlusions were computed
- Page 27 and 28: 7 Conclusions We have presented an
- Page 29: negative function D : S×V↦→
- Page 33: [23] Rick Szeliski and Ramin Zabih.
[7] Yuri Boykov, Olga Veksler, and Ramin Zabih. Markov Random Fields<br />
<strong>with</strong> efficient approximations. In IEEE Conference on Computer Vision<br />
and Pattern Recognition, pages 648–655, 1998.<br />
[8] Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy<br />
minimization <strong>via</strong> graph cuts. IEEE Transactions on Pattern Analysis<br />
and Machine Intelligence, 23(11):1222–1239, November 2001.<br />
[9] L. Ford and D. Fulkerson. Flows in Networks. Princeton University<br />
Press, 1962.<br />
[10] D. Geiger, B. Ladendorf, and A. Yuille. <strong>Occlusions</strong> and binocular stereo.<br />
International Journal of Computer Vision, 14(3):211–226, April 1995.<br />
[11] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions,<br />
and the Bayesian restoration of images. IEEE Transactions on Pattern<br />
Analysis and Machine Intelligence, 6:721–741, 1984.<br />
[12] D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori<br />
estimation for binary images. JournaloftheRoyalStatisticalSociety,<br />
Series B, 51(2):271–279, 1989.<br />
[13] H. Ishikawa and D. Geiger. <strong>Occlusions</strong>, discontinuities, and epipolar<br />
lines in stereo. In European Conference on Computer Vision, pages<br />
232–248, 1998.<br />
[14] H. Ishikawa and D. Geiger. Segmentation by grouping junctions. In<br />
IEEE Conference on Computer Vision and Pattern Recognition, pages<br />
125–131, 1998.<br />
31
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