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NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 1 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
ICIP
2008
IEEE
14 years 11 months ago
Cooperative disparity and object boundary estimation
In this paper we carry out cooperatively both disparity and object boundary estimation by setting the two tasks in a unified Markovian framework. We introduce a new joint probabil...
Ramya Narasimha, Elise Arnaud, Florence Forbes, Ra...
ECCV
2008
Springer
14 years 11 months ago
Analysis of Building Textures for Reconstructing Partially Occluded Facades
Abstract. As part of an architectural modeling project, this paper investigates the problem of understanding and manipulating images of buildings. Our primary motivation is to auto...
Thommen Korah, Christopher Rasmussen
TFS
2008
174views more  TFS 2008»
13 years 9 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
CORR
2000
Springer
129views Education» more  CORR 2000»
13 years 9 months ago
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segm...
Elizabeth Shriberg, Andreas Stolcke, Dilek Z. Hakk...