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CVPR
2012
IEEE
12 years 12 months ago
Learning object class detectors from weakly annotated video
Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object detectors from realworld w...
Alessandro Prest, Christian Leistner, Javier Civer...
PAMI
2008
302views more  PAMI 2008»
14 years 9 months ago
Learning to Detect Moving Shadows in Dynamic Environments
We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a stat...
Ajay J. Joshi, Nikolaos Papanikolopoulos
CVPR
1998
IEEE
15 years 11 months ago
Background Modeling for Segmentation of Video-Rate Stereo Sequences
Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for ...
Christopher K. Eveland, Kurt Konolige, Robert C. B...
96
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ECAI
2010
Springer
14 years 6 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
TNN
2008
178views more  TNN 2008»
14 years 9 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen