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TFS
2008
174views more  TFS 2008»
13 years 7 months ago
Type-2 Fuzzy Markov Random Fields and Their Application to Handwritten Chinese Character Recognition
In this paper, we integrate type-2 (T2) fuzzy sets with Markov random fields (MRFs) referred to as T2 FMRFs, which may handle both fuzziness and randomness in the structural patter...
Jia Zeng, Zhi-Qiang Liu
CVPR
1998
IEEE
14 years 9 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generaliz...
Yuri Boykov, Olga Veksler, Ramin Zabih
ICPR
2000
IEEE
14 years 7 hour ago
Hidden Markov Random Field Based Approach for Off-Line Handwritten Chinese Character Recognition
This paper presents a Hidden Markov Mesh Random Field (HMMRF) based approach for off-line handwritten Chinese characters recognition using statistical observation sequences embedd...
Qing Wang, Rongchun Zhao, Zheru Chi, David Dagan F...
MIA
2010
170views more  MIA 2010»
13 years 2 months ago
Linear intensity-based image registration by Markov random fields and discrete optimization
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...
ICCV
2011
IEEE
12 years 7 months ago
Decision Tree Fields
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fiel...
Sebastian Nowozin, Carsten Rother, Shai Bagon, Ban...