Journal ArticleDOI
On axiomatic characterization of some non-additive measures of information
G. C. Patni,K. C. Jain +1 more
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An axiomatic characterization of non-additive measures of information associated with a pair of probability distributions having the same number of elements has been given and this quantity under additional suitable postulates leads to the non- additive Entropy, Directed-Divergence and Inaccuracy of one or more parameters.Abstract:
An axiomatic characterization of non-additive measures of information associated with a pair of probability distributions having the same number of elements has been given. This quantity under additional suitable postulates leads to the non-additive Entropy, Directed-Divergence and Inaccuracy of one or more parameters.read more
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Proceedings ArticleDOI
An Analysis on Color Preservation Using Non-Shannon Entropy Measures for Gray and Color Images
Shruti Mathur,Mukesh Kumar Gupta +1 more
TL;DR: An approach for color image segmentation, which approximately preserves the colors in different segments, is presented and simulation results of image segmentations using different entropy measures are presented.
Proceedings ArticleDOI
A comparative analysis of entropy based segmentation with Otsu method for gray and color images
Hitesh Sen,Ankit Agarwal +1 more
TL;DR: In this thesis, threshold selection is done on the basis of different entropy measures on both grayscale and color images to obtain image segmentation and it is observed that Havrda-Charvat entropy measure is better matched with Otsu Method than other entropy measures.
Journal ArticleDOI
An axiomatic characterization of nonadditive information improvement
TL;DR: A generalized nonadditive information improvement satisfying nonadditivity and containing the parameters α,β,γ has been axiomatically characterized by a general method and the particular cases of the new measure have been studied.
Proceedings ArticleDOI
Feature Extraction Based Classification of Magnetic Resonance Images Using Machine Learning
TL;DR: The texture of images taken of the brain is analyzed to find the values of various features of the images and the optimal number of principal components to train SVM and KNN classifiers are found.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Journal Article
The mathematical theory of communication
Claude E. Shannon,Warren Weaver +1 more
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
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