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DIS
2006
Springer
15 years 10 months ago
Mining Sectorial Episodes from Event Sequences
In this paper, we introduce a sectorial episode of the form C r, where C is a set of events and r is an event. The sectorial episode C r means that every event of C is followed b...
Takashi Katoh, Kouichi Hirata, Masateru Harao
NIPS
2008
15 years 8 months ago
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
BMVC
1998
15 years 7 months ago
Building Shape Models from Image Sequences using Piecewise Linear Approximation
A method of extracting, classifying and modelling non-rigid shapes from an image sequence is presented. Shapes are approximated by polygons where the number of sides is related to...
Derek R. Magee, Roger D. Boyle
ISMB
1993
15 years 7 months ago
Transmembrane Segment Prediction from Protein Sequence Data
Weconsider tile automatedidentification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segrrmnts) werc examined, representing every protein ...
Sholom M. Weiss, Dawn M. Cohen, Nitin Indurkhya
IJCV
1998
103views more  IJCV 1998»
15 years 6 months ago
Dense Non-Rigid Motion Estimation in Sequences of Medical Images Using Differential Constraints
We describe a new method for computing the displacement vector field in time sequences of 2D or 3D images (4D data). The method is energy-minimizing on the space of correspondence...
Serge Benayoun, Nicholas Ayache