Abstract
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For this purpose, some methods are available based on Shannon and Tsallis entropies. In this paper, we discuss them and propose a method based on Kaniadakis entropy.
Highlights
The concept of entropy was born in thermodynamics and statistical mechanics
Among the other formulations of entropy [5], here we propose the thresholding using Kaniadakis entropy, which is a quite attractive entropy based on the relativistic formulation of the statistical mechanics [6,7]
In this “measure”, we evaluate the number of edge pixels that separate black and white regions
Summary
The concept of entropy was born in thermodynamics and statistical mechanics. Shannon, in 1948, formulated it for the theory of information, obtaining the “information entropy”. In an intuitive understanding of it [1], this entropy relates to the amount of uncertainty about an event associated with a given probability distribution. Both Tsallis and Kaniadakis entropies have an entropic index. We compare the results we can obtain with Kaniadakis and Tsallis entropies, proposing a “measure” on the output image. In this “measure”, we evaluate the number of edge pixels that separate black and white regions. Let us consider that each of the generated copy of the scene has the same probability, which is equal to 1/WF. Published at: http://www.ijsciences.com/pub/issue/2015-02/ Article Number: v420150209; Online ISSN: 2305-3925; Print ISSN: 2410-4477
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