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83
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TIP
1998
124views more  TIP 1998»
14 years 11 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
15 years 4 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
16 years 14 days 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. ...
102
Voted
NIPS
2008
15 years 1 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...
95
Voted
ECML
2006
Springer
15 years 3 months ago
Combinatorial Markov Random Fields
Abstract. A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatoria...
Ron Bekkerman, Mehran Sahami, Erik G. Learned-Mill...