Joint entropy

In information theory, joint entropy is a measure of the uncertainty associated with a set of variables.
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Topic mentions per year

1979-2017
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Papers overview

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2012
2012
A fine grain scalable coding for audio signals is proposed where the entropy coding of the quantizer outputs is made scalable. By… (More)
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2009
2009
Distributed Denial of service (DDoS) attack has become one of the most serious threats to the Internet. DDoS attack can be… (More)
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2009
2009
We introduce a new hybrid approach for spline-based elastic registration of multimodal medical images. The approach uses point… (More)
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2009
2009
Information theoretic measures to incorporate anatomical priors have been explored in the field of emission tomography, but not… (More)
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2008
2008
We developed a maximum a posterior (MAP) reconstruction method for PET image reconstruction incorporating MR image information… (More)
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2007
2007
New upper and lower bounds are given for joint entropy of a collection of random variables, in both discrete and continuous… (More)
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Highly Cited
2005
Highly Cited
2005
  • Doug las Lind
  • 2005
We compute the joint entropy of d commuting automorphisms of a compact metrizable group. Let R d = Z [ u ( 1 . . . . . uf 1] be… (More)
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Review
1999
Review
1999
This paper is concerned with the development of entropy-based registration criteria for automated 3D multi-modality medical image… (More)
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Highly Cited
1998
Highly Cited
1998
Given n discrete random variables = fX1; ; Xng, associated with any subset of f1; 2; ; ng, there is a joint entropy H(X ) where X… (More)
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Highly Cited
1994
Highly Cited
1994
In this paper, we present a new image thresholding technique which uses the relative entropy (also known as the Kullback-Leiber… (More)
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