Determining the Entropic Index q of Tsallis Entropy in Images through Redundancy

被引：36

作者：

Ramirez-Reyes, Abdiel ^{[1
]}

Raul Hernandez-Montoya, Alejandro ^{[1
,2
]}

Herrera-Corral, Gerardo ^{[3
]}

Dominguez-Jimenez, Ismael ^{[1
]}

机构：

[1] CINVESTAV IPN, PhD Program Sci Technol & Soc, AP 14-740, Mexico City 07000, DF, Mexico

[2] Univ Veracruz, Ctr Res Artificial Intelligence, Sebastian Camacho 5, Xalapa 91000, Veracruz, Mexico

[3] CINVESTAV IPN, Dept Phys, AP 14-740, Mexico City 07000, DF, Mexico

来源：

ENTROPY | 2016年 / 18卷 / 08期

关键词：

Shannon entropy; Tsallis entropy; entropic index q; information theory; redundancy; maximum entropy principle; image thresholding;

D O I：

10.3390/e18080299

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The Boltzmann-Gibbs and Tsallis entropies are essential concepts in statistical physics, which have found multiple applications in many engineering and science areas. In particular, we focus our interest on their applications to image processing through information theory. We present in this article a novel numeric method to calculate the Tsallis entropic index q characteristic to a given image, considering the image as a non-extensive system. The entropic index q is calculated through q-redundancy maximization, which is a methodology that comes from information theory. We find better results in the image processing in the grayscale by using the Tsallis entropy and thresholding q instead of the Shannon entropy.

引用

页数：14

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