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» Document Image Binarisation Using Markov Field Model
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CVPR
2000
IEEE
14 years 9 months ago
Representation and Recognition of Complex Human Motion
The quest for a vision system capable of representing and recognizing arbitrary motions benefits from a low dimensional, non-specific representation of flow fields, to be used in ...
Jesse Hoey, James J. Little
ICCV
2009
IEEE
1069views Computer Vision» more  ICCV 2009»
15 years 16 days 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
2010
IEEE
14 years 3 months ago
A Generative Perspective on MRFs in Low-Level Vision
Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while applica...
Uwe Schmidt, Qi Gao, Stefan Roth
CVPR
2004
IEEE
14 years 9 months ago
Efficient Belief Propagation for Early Vision
Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph...
Pedro F. Felzenszwalb, Daniel P. Huttenlocher
CVPR
2009
IEEE
15 years 2 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...