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NIPS
2004
13 years 10 months ago
Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution
Capturing dependencies in images in an unsupervised manner is important for many image processing applications. We propose a new method for capturing nonlinear dependencies in ima...
Hyun-Jin Park, Te-Won Lee
TIP
2002
179views more  TIP 2002»
13 years 8 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki
ICA
2004
Springer
14 years 2 months ago
Unraveling Spatio-temporal Dynamics in fMRI Recordings Using Complex ICA
Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented...
Jörn Anemüller, Jeng-Ren Duann, Terrence...
IJCNN
2008
IEEE
14 years 3 months ago
Unsupervised learning of dependencies between local luminance and contrast in natural images
Abstract— Separate processing of local luminance and contrast in biological visual systems has been argued to be due to the independence of these two properties in natural image ...
Jussi T. Lindgren, Jarmo Hurri, Aapo Hyvärine...
ICASSP
2011
IEEE
13 years 16 days ago
Automatic target classification in SAR images using MPCA
Multilinear analysis provides a powerful mathematical framework for analyzing synthetic aperture radar (SAR) images resulting from the interaction of multiple factors like sky lum...
Tristan Porges, Gérard Favier