A comparative study of energy minimization methods for Markov random fields with smoothness-based priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting...
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Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 30(2008), 6 vom: 20. Juni, Seite 1068-80
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| Auteur principal: |
Szeliski, Richard
(Auteur) |
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| Autres auteurs: |
Zabih, Ramin,
Scharstein, Daniel,
Veksler, Olga,
Kolmogorov, Vladimir,
Agarwala, Aseem,
Tappen, Marshall,
Rother, Carsten |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2008
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Comparative Study
Evaluation Study
Journal Article |
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