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» Learning Features by Contrasting Natural Images with Noise
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CVPR
2005
IEEE
14 years 9 months ago
Fields of Experts: A Framework for Learning Image Priors
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Stefan Roth, Michael J. Black
ICPR
2006
IEEE
14 years 9 months ago
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (Isomap) is one of the representative techniques of man...
Jie Chen, Ruiping Wang, Shiguang Shan, Wen Gao, Xi...
ICCV
2011
IEEE
12 years 7 months ago
Building a better probabilistic model of images by factorization
We describe a directed bilinear model that learns higherorder groupings among features of natural images. The model represents images in terms of two sets of latent variables: one...
Jack Culpepper, Jascha Sohl-Dickstein, Bruno Olaha...
BMVC
2010
13 years 5 months ago
Back to the Future: Learning Shape Models from 3D CAD Data
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
Michael Stark, Michael Goesele, Bernt Schiele
ICA
2010
Springer
13 years 9 months ago
Binary Sparse Coding
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...
Marc Henniges, Gervasio Puertas, Jörg Bornsch...