Fields of experts for image-based rendering

BMVC 2006 - Proceedings of the British Machine Vision Conference 2006

Volumn , Issue , 2006, Pages 1109-1118

  Woodford, O J ^a   Reid, I D ^a   Torr, P H S ^b   Fitzgibbon, A W ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b OXFORD BROOKES UNIVERSITY   (United Kingdom)

^c MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IMAGE RECONSTRUCTION; MARKOV PROCESSES; SIMULATED ANNEALING;

IMAGE PATCHES; IMAGE-BASED RENDERING; MARKOV RANDOM FIELDS; NON-PARAMETRIC MODEL; NOVEL VIEW SYNTHESIS; OPTIMIZATION ALGORITHMS; SIMULATED ANNEALING ALGORITHMS; STATISTICAL PHYSICS;

COMPUTER VISION;

EID: 84898045727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. A. Barbu and S. Zhu. Graph partition by Swendsen-Wang cuts. In Proc. ICCV, Volume 1, pages 320-327, 2003.
2. J. Besag. On the statistical analysis of dirty pictures. J. of the Royal Stat. Soc. B, 48(3):259-302, 1986.
3. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient belief propagation for early vision. In Proc. CVPR, pages 261-268, 2004.
4. A. Fitzgibbon, Y. Wexler, and A. Zisserman. Image-based rendering using image-based priors. In Proc. ICCV, Volume 2, pages 1176-1183, Oct 2003.
5. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE PAMI, 6(6):721-741, Nov 1984.
6. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.
7. J. Huang and D. Mumford. Statistics of natural images and models. In Proc. CVPR, pages 1541-1547, 1999.
8. V. Kolmogorov and R. Zabih. Multi-camera scene reconstruction via graph cuts. In Proc. ECCV, Volume 3, page 82, 2002.
9. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? IEEE PAMI, 26(2):147-159, 2004.
10. H. R. Lourenço, O. C. Martin, and T. Stützle. Iterated local search. In Handbook of Metaheuristics, pages 321-353, 2002.
11. O. C. Martin and S. W. Otto. Partitioning of unstructured meshes for load balancing. Concurrency: Practice and Experience, 7(4):303-314, 1995.
12. R. M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, University of Toronto, 1993.
13. T. Poggio, V. Torre, and C. Koch. Computational vision and regularization theory. Nature, 317:314-319, 1985.
14. S. Roth and M. J. Black. Fields of experts: A framework for learning image priors. In Proc. CVPR, Volume 2, pages 860-867, 2005.
15. S. Roth and M. J. Black. On the spatial statistics of optical flow. In Proc. ICCV, pages 42-49, 2005.
16. S. M. Seitz and C. R. Dyer. Photorealistic scene reconstruction by voxel coloring. In Proc. CVPR, pages 1067-1073, 1997.
17. M. Welling, G. Hinton, and S. Osindero. Learning sparse topographic representations with products of student-t distributions. In NIPS, pages 1359-1366, 2003.
18. O. Woodford and A. W. Fitzgibbon. Fast image-based rendering using hierarchical image-based priors. In Proc. BMVC., Volume 1, pages 260-269, 2005.