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ML
1998
ACM
139views Machine Learning» more  ML 1998»
13 years 5 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
ICIP
2006
IEEE
14 years 7 months ago
A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape
In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting ...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
ICPR
2002
IEEE
14 years 6 months ago
Facial Expression Recognition Using Pseudo 3-D Hidden Markov Models
In this paper pseudo 3-D Hidden Markov Models (P3DHMMs) are applied to the task of dynamic facial expression recognition. P3DHMMs are an extension of the pseudo 2-D case, which ha...
Frank Hülsken, Frank Wallhoff, Gerhard Rigoll...
SSIAI
2000
IEEE
13 years 10 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
PR
2010
147views more  PR 2010»
13 years 4 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani