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» Learning Models for Object Recognition
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NIPS
2001
13 years 10 months ago
Probabilistic principles in unsupervised learning of visual structure: human data and a model
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
AR
2007
204views more  AR 2007»
13 years 9 months ago
Action recognition and understanding through motor primitives
In robotics, recognition of human activity has been used extensively for robot task learning through imitation and demonstration. However, there has not been much work on modeling...
Isabel Serrano Vicente, Ville Kyrki, Danica Kragic...
NIPS
1997
13 years 10 months ago
Task and Spatial Frequency Effects on Face Specialization
There is strong evidence that face processing is localized in the brain. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, a...
Matthew N. Dailey, Garrison W. Cottrell
MVA
1996
167views Computer Vision» more  MVA 1996»
13 years 10 months ago
Applying a Dynamic Recognition Scheme for Vehicle Recognition in Many Object Traffic Scenes
An adaptive object recognition scheme for image sequences of many object scenes is described. The scheme is applied for t r d c object recognition under ego-motion. The recursive ...
Wlodzimierz Kasprzak, Heinrich Niemann
CVPR
2011
IEEE
13 years 5 months ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...