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» Markov Random Field Modeling in Computer Vision
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CVPR
2009
IEEE
1468views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Hardware-Efficient Belief Propagation
Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation beca...
Chao-Chung Cheng, Chia-Kai Liang, Homer H. Chen, L...
WACV
2007
IEEE
14 years 3 months ago
Extraction of Person Silhouettes from Surveillance Imagery using MRFs
We present a method for the simultaneous detection and segmentation of objects from static images. We employ lowlevel contour features that enable us to learn the coarse object sh...
Vinay Sharma, James W. Davis
AINA
2004
IEEE
14 years 27 days ago
Wide-Area Real-Time Distributed Computing in a Tightly Managed Optical Grid - An Optiputer Vision
: Wide area network (WAN) -based distributed computing (DC) has become an active field of research, especially under the label of grid computing. On the other hand, research on WAN...
K. H. (Kane) Kim
CVPR
2011
IEEE
13 years 4 months ago
A Complete Statistical Inverse Ray Tracing Approach to Multi-view Stereo
This paper presents a complete solution to estimating a scene’s 3D geometry and appearance from multiple 2D images by using a statistical inverse ray tracing method. Instead of ...
Shubao Liu, David Cooper
ICPR
2008
IEEE
14 years 3 months ago
Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
Djamel Bouchaffra