@InProceedings{RodriguesGira:2009:CoQiTs,
author = "Rodrigues, Paulo Sergio and Giraldi, Gilson Ant{\^o}nio",
affiliation = "{Centro Universit{\'a}rio da FEI} and {National Laboratory for
Scientific Computing}",
title = "Computing the q-index for Tsallis Nonextensive Image
Segmentation",
booktitle = "Proceedings...",
year = "2009",
editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22.
(SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Image Segmentation, Tsallis Entropy.",
abstract = "The concept of entropy based on Shannon Theory of nformation has
been applied in the \field of image processing and analysis
Since the work of T. Pun. This concept is based on the traditional
Boltzaman-Gibbs entropy, proposed under the classical
thermodynamic. On the other hand, it is well known that this old
formalism fails to explain some physical system if they have
complex behavior such as long rang interactions and long time
memories. Recently, studies in mechanical statistics have proposed
a new kind of entropy, called Tsallis entropy (or non-extensive
entropy), which has been considered with promising results on
several applications in order to explain such phenomena. The main
feature of Tsallis entropy is the q-index parameter, which is
close related to the degree of system nonextensivity. In 2004 was
proposed the \first algorithm for image segmentation based
on Tsallis entropy. However, the computation of the q-index was
already an open problem. On the other hand, in the \field
of image segmentation it is not an easy task to compare the
quality of segmentation results. This is mainly due to the lack of
an image ground truth based on human reasoning. In this paper, we
propose the \first methodology in the \field of
image segmentation for q-index computation and compare it with
other similar approaches using a human based segmentation ground
truth. The results suggest that our approach is a forward step for
image segmentation algorithms based on Information Theory.",
conference-location = "Rio de Janeiro, RJ, Brazil",
conference-year = "11-14 Oct. 2009",
doi = "10.1109/SIBGRAPI.2009.23",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.23",
language = "en",
ibi = "8JMKD3MGPBW4/35UDE25",
url = "http://urlib.net/ibi/8JMKD3MGPBW4/35UDE25",
targetfile = "PID949547.pdf",
urlaccessdate = "2024, Apr. 29"
}