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» Monte Carlo Localization Using SIFT Features
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AGI
2011
14 years 1 months ago
Imprecise Probability as a Linking Mechanism between Deep Learning, Symbolic Cognition and Local Feature Detection in Vision Pro
A novel approach to computer vision is outlined, involving the use of imprecise probabilities to connect a deep learning based hierarchical vision system with both local feature de...
Ben Goertzel
SCFBM
2008
92views more  SCFBM 2008»
14 years 9 months ago
CRANKITE: A fast polypeptide backbone conformation sampler
Background: CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sample...
Alexei A. Podtelezhnikov, David L. Wild
CIVR
2006
Springer
129views Image Analysis» more  CIVR 2006»
15 years 1 months ago
Retrieving Objects Using Local Integral Invariants
The use of local features in computer vision has shown to be promising. Local features have several advantages including invariance to image transformations, independence of the ba...
Alaa Halawani, Hashem Tamimi
PAMI
2007
194views more  PAMI 2007»
14 years 9 months ago
Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
This paper presents a robust object tracking method via a spatial bias appearance model learned dynamically in video. Motivated by the attention shifting among local regions of a ...
Datong Chen, Jie Yang
ECCV
2002
Springer
15 years 11 months ago
Hierarchical Shape Modeling for Automatic Face Localization
Many approaches have been proposed to locate faces in an image. There are, however, two problems in previous facial shape models using feature points. First, the dimension of the s...
Ce Liu, Heung-Yeung Shum, Changshui Zhang