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» Learning Models for Object Recognition
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CVPR
2005
IEEE
15 years 12 months ago
Video Epitomes
Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In...
Vincent Cheung, Brendan J. Frey, Nebojsa Jojic
ITICSE
2003
ACM
15 years 3 months ago
Structuring CSed research studies: connecting the pieces
Changing conditions for teaching increase our motivation to understand the teaching and learning process. First time investigators of educational settings often feel uncertain abo...
Arnold Neville Pears, Mats Daniels
JAIR
1998
198views more  JAIR 1998»
14 years 9 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
ECCV
2008
Springer
14 years 11 months ago
Unsupervised Classification and Part Localization by Consistency Amplification
We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, som...
Leonid Karlinsky, Michael Dinerstein, Dan Levi, Sh...
MM
2009
ACM
209views Multimedia» more  MM 2009»
15 years 4 months ago
Brain state decoding for rapid image retrieval
Human visual perception is able to recognize a wide range of targets under challenging conditions, but has limited throughput. Machine vision and automatic content analytics can p...
Jun Wang, Eric Pohlmeyer, Barbara Hanna, Yu-Gang J...