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UAI
1996
15 years 1 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
107
Voted
PAMI
2010
124views more  PAMI 2010»
14 years 6 months ago
Structural Approach for Building Reconstruction from a Single DSM
We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a li...
Florent Lafarge, Xavier Descombes, Josiane Zerubia...
ICASSP
2011
IEEE
14 years 3 months ago
A Bernoulli-Gaussian model for gene factor analysis
This paper investigates a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for gene factor analysis. Each sample in the dataset is decomposed as a linear combination...
Cecile Bazot, Nicolas Dobigeon, Jean-Yves Tournere...
ICMCS
2005
IEEE
194views Multimedia» more  ICMCS 2005»
15 years 5 months ago
An hardware architecture for 3D object tracking and motion estimation
We present a method to track and estimate the motion of a 3D object with a monocular image sequence. The problem is based on the state equations and is solved by a sequential Mont...
Patrick Lanvin, Jean-Charles Noyer, Mohammed Benje...
TIP
2010
164views more  TIP 2010»
14 years 6 months ago
A Marked Point Process for Modeling Lidar Waveforms
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful ...
Clément Mallet, Florent Lafarge, Michel Rou...