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ICPR
2010
IEEE
13 years 8 months ago
High-Level Feature Extraction Using SIFT GMMs and Audio Models
—We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs) and audio models. SIFT features were extracted from all the im...
Nakamasa Inoue, Tatsuhiko Saito, Koichi Shinoda, S...
CORR
2008
Springer
129views Education» more  CORR 2008»
13 years 8 months ago
Hierarchical Bayesian sparse image reconstruction with application to MRFM
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gau...
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne...
NIPS
2008
13 years 10 months ago
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
BMVC
2010
13 years 6 months ago
Local Gaussian Processes for Pose Recognition from Noisy Inputs
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they ...
Martin Fergie, Aphrodite Galata
ICPR
2002
IEEE
14 years 9 months ago
Probabilistic Models for Generating, Modelling and Matching Image Categories
In this paper we present a probabilistic and continuous framework for supervised image category modelling and matching as well as unsupervised clustering of image space into image...
Hayit Greenspan, Shiri Gordon, Jacob Goldberger