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» Multiscale Conditional Random Fields for Image Labeling
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ICIP
2004
IEEE
14 years 10 months ago
Unsupervised motion detection using a markovian temporal model with global spatial constraints
In this work, we propose an unsupervised Bayesian model for the detection of moving objects from dynamic scenes. This unsupervised solution is a three-step approach that uses a st...
Pierre-Marc Jodoin, Max Mignotte
CVPR
2010
IEEE
1373views Computer Vision» more  CVPR 2010»
14 years 5 months ago
Harmony Potentials for Joint Classification and Segmentation
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales....
Xavier Boix, Josep M. Gonfaus, Joost van de Weijer...
ICPR
2006
IEEE
14 years 9 months ago
Illumination Invariant Texture Retrieval
Two fast illumination invariant image retrieval methods for scenes comprising textured objects with variable illumination are introduced. Both methods are based on texture gradien...
Michal Haindl, Pavel Vacha
KDD
2006
ACM
162views Data Mining» more  KDD 2006»
14 years 9 months ago
Simultaneous record detection and attribute labeling in web data extraction
Recent work has shown the feasibility and promise of templateindependent Web data extraction. However, existing approaches use decoupled strategies ? attempting to do data record ...
Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Y...
FOIKS
2008
Springer
14 years 5 months ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn