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NIPS
2001
13 years 6 months ago
The g Factor: Relating Distributions on Features to Distributions on Images
We describe the g-factor which relates probability distributions on image features to distributions on the images themselves. The g-factor depends only on our choice of features a...
James M. Coughlan, Alan L. Yuille
LION
2007
Springer
113views Optimization» more  LION 2007»
13 years 11 months ago
Limited-Memory Techniques for Sensor Placement in Water Distribution Networks
Abstract. The practical utility of optimization technologies is often impacted by factors that reflect how these tools are used in practice, including whether various real-world c...
William E. Hart, Jonathan W. Berry, Erik G. Boman,...
ICIP
2003
IEEE
14 years 6 months ago
Distributed image compression for sensor networks using correspondence analysis and super-resolution
We outline a distributed codingtechnique for images captured from sensors with overlapping fields of view in a sensor network. First, images from correlated views are roughly regi...
R. Wagner, Robert D. Nowak, Richard G. Baraniuk
CVPR
2008
IEEE
14 years 7 months ago
Unsupervised estimation of segmentation quality using nonnegative factorization
We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the...
Roman Sandler, Michael Lindenbaum
ICIAP
1997
ACM
13 years 9 months ago
Unsupervised Texture Segmentation Using Feature Distributions
This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent i...
Timo Ojala, Matti Pietikäinen