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IPMI
2001
Springer
16 years 7 months ago
Spatio-temporal Covariance Model for Medical Images Sequences: Application to Functional MRI Data
Spatial and temporal correlations which affect the signal measured in functional MRI (fMRI) are usually not considered simultaneously (i.e., as non-independent random processes) in...
Frithjof Kruggel, Habib Benali, Mélanie P&e...
ISBI
2002
IEEE
16 years 7 months ago
Joint estimation of cardiac kinematics and material parameters from noisy imaging data and uncertain mechanical model
There have been many efforts using image analysis algorithms to study cardiac kinematics, or using biomechanics strategies to study myocardial material properties. In this paper, ...
Huafeng Liu, Edward W. B. Lo, Pengcheng Shi
COLT
2005
Springer
15 years 12 months ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
AAAI
2008
15 years 8 months ago
Hidden Dynamic Probabilistic Models for Labeling Sequence Data
We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external...
Xiaofeng Yu, Wai Lam
TMM
2002
104views more  TMM 2002»
15 years 6 months ago
Spatial contextual classification and prediction models for mining geospatial data
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...