GDL

A Markov Random Field Model and Method to Image Matching

2011
Journal from gdlhub / 2017-08-14 11:52:31
Oleh : Mohammed Ouali, Holger Lange, Kheireddine Bouazza, IAJIT
Dibuat : 2012-06-22, dengan 1 file

Keyword : Disparity, MRF, image matching, stereo constraints, resemblance, epipolar geometry, uniqueness, and continuity
Subjek : A Markov Random Field Model and Method to Image Matching
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.6/2955-4.pdf
Sumber pengambilan dokumen : Internet

In this paper, the correspondence problem is solved by minimizing an energy functional using a stochastic


approach. Our procedure generally follows Geman and Geman’s Gibbs sampler for Markov Random Fields (MRF). We


propose a transition generator to generate and explore states. The generator allows constraints such as epipolar, uniqueness,


and order to be imposed. We also propose to embed occlusions in the model. The energy functional is designed to take into


account resemblance, continuity, and number of occlusions. The disparity and occlusion maps as modeled by their energy


functional, i.e., as a Gibbs(Boltzmann distribution, are viewed as a MRF where the matching solution is an optimal state.

Deskripsi Alternatif :

In this paper, the correspondence problem is solved by minimizing an energy functional using a stochastic


approach. Our procedure generally follows Geman and Geman’s Gibbs sampler for Markov Random Fields (MRF). We


propose a transition generator to generate and explore states. The generator allows constraints such as epipolar, uniqueness,


and order to be imposed. We also propose to embed occlusions in the model. The energy functional is designed to take into


account resemblance, continuity, and number of occlusions. The disparity and occlusion maps as modeled by their energy


functional, i.e., as a Gibbs(Boltzmann distribution, are viewed as a MRF where the matching solution is an optimal state.

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