Segmentação de Imagens por entropia relativa não extensiva (Image Segmentation using nonextensive relative entropy)

Marcio Portes de Albuquerque (, Israel Andrade Esquef (, Marcelo Portes de Albuquerque (

1UENF - Universidade Estadual Norte Fluminense
2CBPF - Centro Brasileiro de Pesquisas Físicas

This paper appears in: Revista IEEE América Latina

Publication Date: Sept. 2008
Volume: 6,   Issue: 5 
ISSN: 1548-0992

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 nonextensive 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 nonextensive relative entropy.

Index Terms:
image segmentation, nonextensive entropy, relative nonextensive entropy   

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