By Topic

Image Segmentation using nonextensive relative entropy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
de Albuquerque, M.P. ; Centro Brasileiro de Pesquisas Fisicas (CBPF), Ministerio da Cienc. e Tecnol. do Brasil, Urca ; Esquef, I.A. ; de Albuquerque, M.P.

Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The non-extensive entropy, also known as Tsallis entropy, is a recent development in statistical mechanics and has been considered as a useful measure in describing termostatistical properties of physical systems. In this new formalism a real quantity q was introduced as parameter for physical systems that presents long range interactions, long time memories and fractal-type structures. In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. This approach uses the Shannon entropy from the information theory considering the gray level image histogram as a probability distribution. In this work, it was applied the Tsallis entropy as a generalized entropy formalism for information theory. For the first time it was proposed an image thresholding method using a non-extensive relative entropy.

Published in:

Latin America Transactions, IEEE (Revista IEEE America Latina)  (Volume:6 ,  Issue: 5 )