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» Markov Random Field Modeling in Computer Vision
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UAI
2004
13 years 11 months ago
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Iain Murray, Zoubin Ghahramani
CVPR
2010
IEEE
14 years 5 months ago
Morphable Reflectance Fields for Enhancing Face Recognition
In this paper, we present a novel framework to address the confounding effects of illumination variation in face recognition. By augmenting the gallery set with realistically re...
Ritwik Kumar, Michael Jones, Tim Marks
PAMI
2008
161views more  PAMI 2008»
13 years 9 months ago
Multilayered 3D LiDAR Image Construction Using Spatial Models in a Bayesian Framework
Standard 3D imaging systems process only a single return at each pixel from an assumed single opaque surface. However, there are situations when the laser return consists of multip...
Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J...
ICCAD
2003
IEEE
127views Hardware» more  ICCAD 2003»
14 years 6 months ago
A Probabilistic-Based Design Methodology for Nanoscale Computation
As current silicon-based techniques fast approach their practical limits, the investigation of nanoscale electronics, devices and system architectures becomes a central research p...
R. Iris Bahar, Joseph L. Mundy, Jie Chen
IPPS
1998
IEEE
14 years 1 months ago
Implementing Parallelism in Random Discrete Event-Driven Simulation
Abstract. The inherently sequential nature of random discrete eventdriven simulation has made parallel and distributed processing di cult. This paper presents a method of applying ...
Marc Bumble, Lee D. Coraor