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» Learning Models for Object Recognition
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ICANN
2009
Springer
15 years 8 months ago
Learning from Examples to Generalize over Pose and Illumination
We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples of a subset of p...
Marco K. Müller, Rolf P. Würtz
133
Voted
EMNLP
2006
15 years 4 months ago
Learning Field Compatibilities to Extract Database Records from Unstructured Text
Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper ...
Michael L. Wick, Aron Culotta, Andrew McCallum
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
15 years 10 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
CVIU
2004
143views more  CVIU 2004»
15 years 3 months ago
Shape matching of partially occluded curves invariant under projective transformation
This paper describes a method to identify partially occluded shapes which are randomly oriented in 3D space. The goal is to match the object contour present in an image with an ob...
Carlos Orrite, José Elías Herrero Ja...
AMFG
2003
IEEE
126views Biometrics» more  AMFG 2003»
15 years 8 months ago
Rank Constrained Recognition under Unknown Illuminations
Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what ...
Shaohua Kevin Zhou, Rama Chellappa