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WACV
2005
IEEE
13 years 11 months ago
Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
Andrew N. Stein, Martial Hebert
CORR
2010
Springer
108views Education» more  CORR 2010»
13 years 3 months ago
Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics
We revisit the problem of model-based object recognition for intensity images and attempt to address some of the shortcomings of existing Bayesian methods, such as unsuitable prior...
Vasileios Zografos, Bernard F. Buxton
IVCNZ
1998
13 years 7 months ago
Feature point Detection in Blurred Images
: Feature point FP detection is an important pre-processing step in image registration, data fusion, object recognition and in many other tasks. This paper deals with multiframe FP...
Jaroslav Kautsky, Barbara Zitová, Jan Fluss...
CVPR
2009
IEEE
15 years 1 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
ACIVS
2006
Springer
13 years 10 months ago
Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform
Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the...
Seung-Bin Im, Sung-Bae Cho