Tsallis Entropy In Bi-level And Multi-level Image Thresholding

Sparavigna, Amelia Carolina

doi:10.18483/ijsci.613

Cited by 11 publications

(2 citation statements)

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“…The clip-level has a crucial role in the rendering of the binary image; often it is determined by means of the entropy, evaluated on the histogram of grey-tones of the pixels [16][17][18].…”

Section: Methods Of Segmentationmentioning

confidence: 99%

Image Segmentation Applied to the Study of Micrographs of Cellular Solids

Sparavigna¹

2017

ijSciences

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Abstract:The paper is proposing a method of image segmentation applied to the study of the micrographs of cellular solids. The segmentation is based on a thresholding which creates a binary (black and white) image of the micrograph. The binary image is divided in super-pixels which correspond to the microcells of the material. From the areas of the super-pixels it is easy to evaluate the distribution of the size of the cells and correlate this distribution to the properties of the material.

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Section: Methods Of Segmentationmentioning

confidence: 99%

Image Segmentation Applied to the Study of Micrographs of Cellular Solids

Sparavigna¹

2017

ijSciences

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“…Let us assume some thresholds t 1 , t 2 … t m for the gray levels. Some classes should be introducing; their probability distributions are by Sparavigna [9] as follow:…”

Section: Tsallis Entropy Criterionmentioning

confidence: 99%

Multi-Objective Optimization for Worldview Image Segmentation Funded on the Entropies of Tsallis and Rényi

Mechkouri¹

2019

IJATCSE

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The article describes a new method of threshold satellite image, based on the optimization multi-objective for segmentation of Worldview images and funded on the Tsallis and the Rényi entropies, allowing the evolution of the satellite image classification. Owing to the goal of achieving a large classifying all unclassified pixels by the previous method in 2017 in our research laboratory. An improved analysis and a multi-objective optimized thresholding method was proposed. Firstly, we are calculate the optimal thresholds with respect to the criteria retained such as the Tsallis criterion and the Rényi criterion. Lastly, we are challenging the performance of our method to that developed previously in 2017. The new method effectiveness evaluation confirmed by the calculation of the evaluation criterion related on both the Levine and Nazif criterion and the Mean Squared Error criterion. The results obtained by our approach were very satisfactory. It was been shown that the method overcomes the difficulties of the method previously developed in 2017 and obtained results that are more precise. In particular, for synthetic images, the segmentation accuracy increases by 81.16% and for the satellite images, the segmentation also improves enormously, and the accuracy of the overall classification of Worldview images increases by 97.21%. Therefore, the new method based on multi-objective optimization contribute significantly to performance.

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