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» Decomposition of range images using markov random fields
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UAI
2008
13 years 9 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller
ICCV
2007
IEEE
14 years 9 months ago
Steerable Random Fields
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
Stefan Roth, Michael J. Black
ISCAS
1994
IEEE
104views Hardware» more  ISCAS 1994»
13 years 11 months ago
A Homotopy Continuation Method for Parameter Estimation in MRF Models and Image Restoration
In this paper, we present an alternate approach to estimate the parameters of a Markov random field (MRF) model for images using the concepts of homotopy continuation method. We a...
P. K. Nanda, Uday B. Desai, P. G. Poonacha
AAAI
2004
13 years 9 months ago
Reconstruction of 3D Models from Intensity Images and Partial Depth
This paper addresses the probabilistic inference of geometric structures from images. Specifically, of synthesizing range data to enhance the reconstruction of a 3D model of an in...
Luz Abril Torres-Méndez, Gregory Dudek
ICIP
2000
IEEE
14 years 9 months ago
Definition of a Spatial Entropy and its Use for Texture Discrimination
This paper presents a new definition of a spatial entropy mainly based on the Markov Random Field (MRF) properties. Starting with the study of the entropy proposed in [1] for the ...
Florence Tupin, Henri Maître, Marc Sigelle