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APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
13 years 8 months ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan
ICIP
2009
IEEE
14 years 6 months ago
A Markov Random Field Model for Extracting Near-Circular Shapes
We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We...
Tamas Blaskovics, Zoltan Kato, and Ian Jermyn
NAACL
2004
13 years 8 months ago
Accurate Information Extraction from Research Papers using Conditional Random Fields
With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importanc...
Fuchun Peng, Andrew McCallum
3DOR
2008
13 years 9 months ago
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
Guillaume Lavoué, Christian Wolf
CVPR
2008
IEEE
14 years 8 months ago
Learning coupled conditional random field for image decomposition with application on object categorization
This paper proposes a computational system of object categorization based on decomposition and adaptive fusion of visual information. A coupled Conditional Random Field is develop...
Xiaoxu Ma, W. Eric L. Grimson