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ICASSP
2011
IEEE
12 years 8 months ago
A new stochastic image model based on Markov random fields and its application to texture modeling
Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is intr...
Siamak Yousefi, Nasser D. Kehtarnavaz
TIP
1998
124views more  TIP 1998»
13 years 4 months ago
Texture synthesis via a noncausal nonparametric multiscale Markov random field
Abstract— Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesising and capturing the characteristics of a wide variety of textures, f...
Rupert Paget, I. Dennis Longstaff
SSIAI
2000
IEEE
13 years 9 months ago
Pairwise Markov Random Fields and its Application in Textured Images Segmentation
The use of random fields, which allows one to take into account the spatial interaction among random variables in complex systems, is a frequent tool in numerous problems of stati...
Wojciech Pieczynski, Abdel-Nasser Tebbache
ICML
2001
IEEE
14 years 5 months ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
NIPS
2008
13 years 6 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...