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Image thresholding through nonextensive entropies and long-range correlation

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Abstract

In recent years, many image thresholding techniques have emerged involving entropy measures with the related long-range and short-range correlation properties. However, despite the segmentation capabilities demonstrated by those methods, we have noticed limitations in dealing with images with local long-range correlation in the foreground and background. In order to address this issue, in this paper, we propose a combination of two approaches, the first one that applies the Tsallis and Shannon entropies while the second one uses the Masi entropy as the information measure. Such a combination leads to a thresholding criterion based on Tsallis and Masi entropies, providing an improved long-range correlation image thresholding method. Besides, differently from the others, the novel technique works with two entropic parameters instead of just one, which improves the technique’s capabilities to fit the specific requirements of the applications. In the computational experiments, the quantitative evaluation of the segmentation is performed using infrared, Non-Destructive Testing images, the public Berkeley Segmentation Dataset (BSDS500), together with four error metrics computed through the ground-truth segmentation and the obtained results. The proposed method outperforms the competing approaches for infrared and non-destructive images. In the case of BSDS500, we get the second best results. For benchmark images without ground-truth segmentation, the visual analysis shows that the proposal is competitive concerning counterpart techniques.

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Notes

  1. Interestingly, the values of the entropic parameter r used for processing infrared images were not reported by the authors of [57].

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Acknowledgements

The authors would like to especially thank Prof. Congjie Ou for supplying your IR and NDT images, and the ground-truth of them. In addition, for some discussions. We also thank Prof. ZuoYong Li for supplying several images and ground-truth of them.

Funding

This research was partially funded by the National Institute of Science and Technology in Medicine Assisted by Scientific Computing (INCT-MACC), Award Number CNPQ 465586/2014-7 - INCT-MACC, and Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award Number 307769/2020-8, Brazil.

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  1. Department of Mathematics, Federal University of Bahia, Salvador, BA, Brazil

    Perfilino Eugênio Ferreira Júnior & Vinícius Moreira Mello

  2. National Laboratory for Scientific Computing, Coordination of Mathematical and Computational Methods, Petrópolis, RJ, Brazil

    Gilson Antonio Giraldi

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  1. Perfilino Eugênio Ferreira Júnior
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Conceptualization and Methodology: Perfilino Eugênio Ferreira Júnior and Vinícius Moreira Mello. Original draft preparation: Gilson Antonio Giraldi

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Ferreira Júnior, P.E., Mello, V.M. & Giraldi, G.A. Image thresholding through nonextensive entropies and long-range correlation. Multimed Tools Appl 82, 43029–43073 (2023). https://doi.org/10.1007/s11042-023-14978-x

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