The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Exact optimization for markov random fields with convex priors
Abstract
We introduce a method to solve exactly a first order Markov random field optimization problem in more generality than was previously possible. The MRF has a prior term that is convex in terms of a linearly ordered label set. The method maps the problem into a minimum-cut problem for a directed graph, for which a globally optimal solution can be found in polynomial time. The convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method.
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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Rayward-Smith, V. J. (1991) · Journal of the Operational Research Society · doi:10.1057/jors.1991.155 · Cited by 16,821
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Spatial Interaction and the Statistical Analysis of Lattice Systems
Besag, J. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00999.x · Cited by 6,067
Markov Random Fields and Their Applications
Kindermann, R., & Snell, J. L. (1980) · Contemporary Mathematics · doi:10.1090/conm/001 · Cited by 1,290
Exact Maximum A Posteriori Estimation for Binary Images
Greig, D. M., Porteous, B. T., & Seheult, A. H. (1989) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1989.tb01764.x · Cited by 1,055
Using dynamic programming for solving variational problems in vision
Amini, A. A., Weymouth, T. E., & Jain, R. C. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.57681 · Cited by 1,008
A maximum-flow formulation of the N-camera stereo correspondence problem
Roy, S., & Cox, I. J. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710763 · Cited by 485
Dynamic programming for detecting, tracking, and matching deformable contours
Geiger, D., Gupta, A., Costa, L. A., & Vlontzos, J. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368194 · Cited by 429
Cited by
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
What energy functions can be minimized via graph cuts?
Kolmogorov, V., & Zabih, R. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262177 · Cited by 3,127
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Computing geodesics and minimal surfaces via graph cuts
Boykov, & Kolmogorov (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238310 · Cited by 555
Metadata
- Authors
- Ishikawa, H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-10
- Volume
- 25
- Issue
- 10
- Pages
- 1333-1336
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2140502500
- Cited by
- 486
- References
- 16
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
