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» Markov Random Field Modeling in Computer Vision
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VISAPP
2007
13 years 10 months ago
Image deconvolution using a stochastic differential equation approach
We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classi...
Xavier Descombes, M. Lebellego, Elena Zhizhina
VISAPP
2010
13 years 7 months ago
Inverse Problems in Imaging and Computer Vision - From Regularization Theory to Bayesian Inference
phies are also mentioned and a common mathematical abstraction for all these inverses problems will be presented. By focusing on a simple linear forward model, first a synthetic an...
Ali Mohammad-Djafari
ROBOCUP
1999
Springer
102views Robotics» more  ROBOCUP 1999»
14 years 1 months ago
A Method for Localization by Integration of Imprecise Vision and a Field Model
In recent years, many researchers in AI and Robotics pay attention to RoboCup, because robotic soccer games needs various techniques in AI and Robotics, such as navigation, behavi...
Kazunori Terada, Kouji Mochizuki, Atsushi Ueno, Hi...
ICPR
2008
IEEE
14 years 10 months ago
HOPS: Efficient region labeling using Higher Order Proxy Neighborhoods
We present the Higher Order Proxy Neighborhoods (HOPS) approach to modeling higher order neighborhoods in Markov Random Fields (MRFs). HOPS incorporates more context information i...
Albert Y. C. Chen, Jason J. Corso, Le Wang
CVPR
2009
IEEE
15 years 4 months ago
Learning color and locality cues for moving object detection and segmentation
This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limit...
Feng Liu (University of Wisconsin-Madison), Michae...