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ICCV
2011
IEEE
12 years 7 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ECCV
2002
Springer
14 years 9 months ago
Factorial Markov Random Fields
In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous t...
Junhwan Kim, Ramin Zabih
PAMI
2002
108views more  PAMI 2002»
13 years 7 months ago
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...
Derek C. Stanford, Adrian E. Raftery
ICIP
2007
IEEE
14 years 9 months ago
A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs
In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse mat...
Csaba Benedek, Tamas Sziranyi, Zoltan Kato, and Jo...
PAMI
2007
155views more  PAMI 2007»
13 years 7 months ago
Localization of Shapes Using Statistical Models and Stochastic Optimization
—In this paper, we present a new model for deformations of shapes. A pseudolikelihood is based on the statistical distribution of the gradient vector field of the gray level. The...
François Destrempes, Max Mignotte, Jean-Fra...