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» Invariant Image Object Recognition Using Mixture Densities
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MICAI
2010
Springer
14 years 10 months ago
Object Class Recognition Using SIFT and Bayesian Networks
Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, sc...
Leonardo Chang, Miriam Monica Duarte, Luis Enrique...
AIPR
2002
IEEE
15 years 4 months ago
ICA Mixture Model based Unsupervised Classification of Hyperspectral Imagery
Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally ...
Chintan A. Shah, Manoj K. Arora, Stefan A. Robila,...
IJCV
1998
121views more  IJCV 1998»
14 years 11 months ago
Generalization to Novel Views: Universal, Class-based, and Model-based Processing
A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewi...
Yael Moses, Shimon Ullman
ICPR
2000
IEEE
16 years 23 days ago
Experiments with an Extended Tangent Distance
Invariance is an important aspect in image object recognition. We present results obtained with an extended tangent distance incorporated in a kernel density based Bayesian classi...
Daniel Keysers, Hermann Ney, Jörg Dahmen, Tho...
FGR
1998
IEEE
131views Biometrics» more  FGR 1998»
15 years 4 months ago
Tracking and Segmenting People in Varying Lighting Conditions Using Colour
Colour cues were used to obtain robust detection and tracking of people in relatively unconstrained dynamic scenes. Gaussian mixture models were used to estimate probability densi...
Yogesh Raja, Stephen J. McKenna, Shaogang Gong