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BMCBI
2010
214views more  BMCBI 2010»
13 years 8 months ago
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
Background: Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the under...
Aaron M. Newman, James B. Cooper
CVIU
2006
162views more  CVIU 2006»
13 years 8 months ago
Unsupervised scene analysis: A hidden Markov model approach
This paper presents a new approach to scene analysis, which aims at extracting structured information from a video sequence using directly low-level data. The method models the se...
Manuele Bicego, Marco Cristani, Vittorio Murino
CVPR
1998
IEEE
14 years 10 months ago
Subtly Different Facial Expression Recognition and Expression Intensity Estimation
We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. E...
James Jenn-Jier Lien, Takeo Kanade, Jeffrey F. Coh...
MASCOTS
2003
13 years 9 months ago
Derivation of Passage-time Densities in PEPA Models using ipc: the Imperial PEPA Compiler
We present a technique for defining and extracting passage-time densities from high-level stochastic process algebra models. Our high-level formalism is PEPA, a popular Markovian...
Jeremy T. Bradley, Nicholas J. Dingle, Stephen T. ...
RAS
2000
187views more  RAS 2000»
13 years 7 months ago
Detection, tracking, and classification of action units in facial expression
Most of the current work on automated facial expression analysis attempt to recognize a small set of prototypic expressions, such as joy and fear. Such prototypic expressions, how...
James Jenn-Jier Lien, Takeo Kanade, Jeffrey F. Coh...