Disparity from stereo-segment silhouettes of weakly-textured images

British Machine Vision Conference, BMVC 2009 - Proceedings

Volumn , Issue , 2009, Pages

  Dellen, Babette ^a   Wörgötter, Florentin ^b  

^a MAX PLANCK INSTITUTE FOR DYNAMICS AND SELF ORGANIZATION   (Germany)

^b UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER VISION; IMAGE SEGMENTATION;

BOUNDARY POINTS; IMAGE SEGMENTS; MATCHING ALGORITHM; MIDDLEBURY DATASET; STEREO ALGORITHMS; STEREO-IMAGE; WINDOW-BASED;

IMAGE MATCHING;

EID: 84898842490     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.23.96     Document Type: Conference Paper

References (18)

1. M. Blatt, S. Wiseman, and E. Domany. Superparametric clustering of data. Physical Review Letters, 76(18):3251, 1996.
2. Y. Boykov and Vladimir Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, (9):1124-1137, 2004.
3. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(11):1222-1239, 2001.
4. M. Z. Brown, D. Burschka, and G D. Hager. Advances in computational stereo. IEEE Trans. Pattern Analysis and Machine Intelligence, 25(8):993-1008, 2003.
5. B. Dellen and F. Wörgötter. Simulating dynamical systems for early vision. In Int. Conf. Comp. Vis. Theory and Applications (Visapp), Volume 2, pages 525-528, 2009.
6. B. Dellen, G Alenya, S. Foix, and C. Torras. 3d object reconstruction from swiss-ranger sensor data using a spring-mass model. In Int. Conf. Comp. Vis. Theory and Applications (Visapp), Volume 2, pages 368-372, 2009.
7. A. Klaus, M. Sormann, and K. Karner. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In Intl. Conf. Pattern Recognition, pages 15-18, 2006.
8. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):147-159, 2004.
9. R. Opara and F. Wörgötter. A fast and robust cluster update algorithm for image segmentation in spin-lattice models without annealing - visual latencies revisited. Neural Computation, 10:1547-1566, 1998.
10. R. B. Potts. Some generalized order-disorder transformations. Proc. Cambridge Philos. Soc., 48:106-109, 1952.
11. Carsten Rother, Tom Minka, Andrew Blake, and Vladimir Kolmogorov. Cosegmen-tation of image pairs by histogram matching - incorporating a global constraint into mrfs. In CVPR '06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 993-1000, 2006.
12. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47:7-42, 2002.
13. S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of mulit-view stereo reconstruction algorithms. In IEEE Conf. Compu. Vision and Pattern Recognition, pages 519-528, 2006.
14. R.H. Swendsen and S. Wang. Nonuniversal critical dynamics in monte carlo simulations. Physical Review Letters, 76(18):86-88, 1987.
15. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother. A comparative study of energy minimization methods for markov random fields. In Ninth European Conference on Computer Vision, Volume 2, pages 16-29, 2006.
16. Alexander Toshev, Jianbo Shi, and Kostas Daniilidis. Image matching via saliency region correspondences. In IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007.
17. C. von Ferber and F. Wörgötter. Cluster update algorithm and recognition. Physical Review E, 62:1461-1664, 2000.
18. Q. Yang, C. Engels, and A. Akbarzadeh. Near real-time stereo for weakly-textured scenes. In British Machine Vision Conference, 2008.