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NPL
2006
172views more  NPL 2006»
13 years 8 months ago
Adapting RBF Neural Networks to Multi-Instance Learning
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...
Min-Ling Zhang, Zhi-Hua Zhou
ICCV
2011
IEEE
12 years 8 months ago
From Learning Models of Natural Image Patches to Whole Image Restoration
Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can...
Daniel Zoran, Yair Weiss
ICCV
2009
IEEE
15 years 1 months ago
Super-Resolution from a Single Image
Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misali...
Daniel Glasner, Shai Bagon, Michal Irani
ICCV
2009
IEEE
3893views Computer Vision» more  ICCV 2009»
14 years 11 months ago
 Super-Resolution From a Single Image
Methods for super-resolution (SR) can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misa...
Daniel Glasner, Shai Bagon, and Michal Irani
CVPR
2010
IEEE
14 years 17 days ago
Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images
The automatic detection of lung nodules attached to other pulmonary structures is a useful yet challenging task in lung CAD systems. In this paper, we propose a stratified statist...
Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Ki...