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» Markov Random Field Modeling in Computer Vision
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CVPR
2009
IEEE
15 years 4 months ago
An Empirical Bayes Approach to Contextual Region Classification
This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in information-theoretic denoising. The chief novelty of ...
Svetlana Lazebnik (UNC Chapel Hill), Maxim Raginsk...
ICCV
2009
IEEE
1069views Computer Vision» more  ICCV 2009»
15 years 2 months ago
An efficient algorithm for Co-segmentation
This paper is focused on the Co-segmentation problem [1] – where the objective is to segment a similar object from a pair of images. The background in the two images may be ar...
Dorit S. Hochbaum, Vikas Singh
CVPR
2007
IEEE
14 years 11 months ago
Belief Propagation in a 3D Spatio-temporal MRF for Moving Object Detection
Previous pixel-level change detection methods either contain a background updating step that is costly for moving cameras (background subtraction) or can not locate object positio...
Zhaozheng Yin, Robert T. Collins
ICCV
2003
IEEE
14 years 11 months ago
Entropy-of-likelihood Feature Selection for Image Correspondence
Feature points for image correspondence are often selected according to subjective criteria (e.g. edge density, nostrils). In this paper, we present a general, non-subjective crit...
Matthew Toews, Tal Arbel
CVPR
1999
IEEE
14 years 1 months ago
A New Bayesian Framework for Object Recognition
We introduce an approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. This approach provides an effici...
Yuri Boykov, Daniel P. Huttenlocher