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ACIVS
2006
Springer
13 years 11 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
IVC
2007
266views more  IVC 2007»
13 years 7 months ago
Face detection and facial feature localization without considering the appearance of image context
Face and facial feature detection plays an important role in various applications such as human computer interaction, video surveillance, face tracking, and face recognition. Effi...
Suphakant Phimoltares, Chidchanok Lursinsap, Kosin...
WAPCV
2004
Springer
14 years 1 months ago
Learning of Position-Invariant Object Representation Across Attention Shifts
Abstract. Selective attention shift can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by ...
Muhua Li, James J. Clark
ICCV
2005
IEEE
14 years 9 months ago
Object Recognition in High Clutter Images Using Line Features
We present an object recognition algorithm that uses model and image line features to locate complex objects in high clutter environments. Finding correspondences between model an...
Philip David, Daniel DeMenthon
ICPR
2008
IEEE
14 years 2 months ago
Ranking the local invariant features for the robust visual saliencies
Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and ...
Shengping Xia, Peng Ren, Edwin R. Hancock