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BMCBI
2008
93views more  BMCBI 2008»
13 years 7 months ago
Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throug
Background: The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the in...
Zheng Yin, Xiaobo Zhou, Chris Bakal, Fuhai Li, You...
CVPR
2007
IEEE
14 years 9 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...
SSD
2007
Springer
124views Database» more  SSD 2007»
14 years 1 months ago
Querying Objects Modeled by Arbitrary Probability Distributions
In many modern applications such as biometric identification systems, sensor networks, medical imaging, geology, and multimedia databases, the data objects are not described exact...
Christian Böhm, Peter Kunath, Alexey Pryakhin...
PR
2007
107views more  PR 2007»
13 years 7 months ago
Newtonian clustering: An approach based on molecular dynamics and global optimization
Given a data set, a dynamical procedure is applied to the data points in order to shrink and separate, possibly overlapping clusters. Namely, Newton’s equations of motion are em...
Konstantinos Blekas, Isaac E. Lagaris
ISBI
2006
IEEE
14 years 8 months ago
Statistical modeling and EM clustering of white matter fiber tracts
A statistical model of the fiber bundles is calculated as the average and standard deviation of a parametric representation of the fiber tracts, using the coefficients of the 3D q...
Mahnaz Maddah, W. Eric L. Grimson, Simon K. Warfie...