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» Explanation-Based Learning for Image Understanding
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CVPR
2008
IEEE
14 years 9 months ago
Active microscopic cellular image annotation by superposable graph transduction with imbalanced labels
Systematic content screening of cell phenotypes in microscopic images has been shown promising in gene function understanding and drug design. However, manual annotation of cells ...
Jun Wang, Shih-Fu Chang, Xiaobo Zhou, Stephen T. C...
JBI
2007
148views Bioinformatics» more  JBI 2007»
13 years 7 months ago
A method for linking computed image features to histological semantics in neuropathology
In medical image analysis, the image content is often represented by computed features that need to be interpreted at a clinical level of understanding to support lopment of clini...
Birgit Lessmann, Tim W. Nattkemper, V. H. Hans, An...
CCIA
2005
Springer
14 years 1 months ago
Classifying Natural Objects on Outdoor Scenes
We propose an hybrid and probabilistic classification of image regions belonging to scenes primarily containing natural objects, e.g. sky, trees, etc. as a first step in solving ...
Anna Bosch, Xavier Muñoz, Joan Martí...
NECO
2010
154views more  NECO 2010»
13 years 5 months ago
Role of Homeostasis in Learning Sparse Representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that ...
Laurent U. Perrinet
BMCV
2000
Springer
13 years 12 months ago
The Spectral Independent Components of Natural Scenes
Abstract. We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was a...
Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski