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» Multiscale Conditional Random Fields for Image Labeling
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CVPR
2006
IEEE
14 years 10 months ago
The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects
This paper addresses the problem of detecting and segmenting partially occluded objects of a known category. We first define a part labelling which densely covers the object. Our ...
John M. Winn, Jamie Shotton
MIA
2010
170views more  MIA 2010»
13 years 3 months ago
Linear intensity-based image registration by Markov random fields and discrete optimization
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...
FGR
2011
IEEE
288views Biometrics» more  FGR 2011»
13 years 11 days ago
Hierarchical CRF with product label spaces for parts-based models
— Non-rigid object detection is a challenging open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, humancomput...
Gemma Roig, Xavier Boix Bosch, Fernando De la Torr...
ECCV
2008
Springer
14 years 10 months ago
Sparse Long-Range Random Field and Its Application to Image Denoising
Many recent techniques for low-level vision problems such as image denoising are formulated in terms of Markov random field (MRF) or conditional random field (CRF) models. Nonethel...
Yunpeng Li, Daniel P. Huttenlocher
ICCV
2005
IEEE
14 years 10 months ago
A Hierarchical Field Framework for Unified Context-Based Classification
We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional f...
Sanjiv Kumar, Martial Hebert