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» Supervised Image Segmentation Using Markov Random Fields
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TIP
2008
169views more  TIP 2008»
14 years 11 months ago
Weakly Supervised Learning of a Classifier for Unusual Event Detection
In this paper, we present an automatic classification framework combining appearance based features and Hidden Markov Models (HMM) to detect unusual events in image sequences. One...
Mark Jager, Christian Knoll, Fred A. Hamprecht
ISNN
2005
Springer
15 years 5 months ago
MRF-MBNN: A Novel Neural Network Architecture for Image Processing
Contextual information and a priori knowledge play important roles in image segmentation based on neural networks. This paper proposed a method for including contextual information...
Nian Cai, Jie Yang, Kuanghu Hu, Haitao Xiong
CVPR
2010
IEEE
15 years 5 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...
CVPR
2007
IEEE
16 years 1 months ago
Multi-label image segmentation via max-sum solver
We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recent...
Branislav Micusík, Tomás Pajdla
CVPR
2005
IEEE
16 years 1 months ago
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Stefan Roth, Michael J. Black