Image Segmentation using nonextensive relative entropy
| Autor: | M. Portes de Albuquerque, I.A. Esquef, Marcelo Portes de Albuquerque |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
General Computer Science
Nonextensive entropy business.industry Tsallis entropy Principle of maximum entropy ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image processing Image segmentation Joint entropy Entropy estimation Computer Science::Computer Vision and Pattern Recognition Entropy (information theory) Artificial intelligence Electrical and Electronic Engineering business Mathematics |
| Zdroj: | IEEE Latin America Transactions. 6:477-483 |
| ISSN: | 1548-0992 |
| DOI: | 10.1109/tla.2008.4839118 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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