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» Markov Random Field Modeling in Computer Vision
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BMCBI
2007
160views more  BMCBI 2007»
13 years 9 months ago
Identifying protein complexes directly from high-throughput TAP data with Markov random fields
Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms ...
Wasinee Rungsarityotin, Roland Krause, Arno Sch&ou...
CVPR
2010
IEEE
1790views Computer Vision» more  CVPR 2010»
14 years 5 months ago
Data Driven Mean-Shift Belief Propagation For non-Gaussian MRFs
We introduce a novel data-driven mean-shift belief propagation (DDMSBP) method for non-Gaussian MRFs, which often arise in computer vision applications. With the aid of scale sp...
Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
CVPR
2008
IEEE
14 years 11 months ago
Consistent image analogies using semi-supervised learning
In this paper we study the following problem: given two source images A and A , and a target image B, can we learn to synthesize a new image B which relates to B in the same way t...
Li Cheng, S. V. N. Vishwanathan, Xinhua Zhang
ICPR
2008
IEEE
14 years 10 months ago
Image objects and multi-scale features for annotation detection
This paper investigates several issues in the problem of detecting handwritten markings, or annotations, on printed documents. One issue is to define the appropriate units over wh...
Eric Saund, Jindong Chen, Yizhou Wang
CVPR
2010
IEEE
14 years 5 months ago
Vessel Scale Selection using MRF Optimization
Many feature detection algorithms rely on the choice of scale. In this paper, we complement standard scaleselection algorithms with spatial regularization. To this end, we formula...
Hengameh Mirzaalian, Ghassan Hamarneh