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An active contour model for texture image segmentation using Rényi divergence measure

    Sidi Yassine Idrissi   Affiliation

Abstract

This paper proposes an efficient method for active unsupervised texture segmentation. A new descriptor for texture features extractions based on Gaussian and mean curvature is constructed. Then the optimization of a functional who uses the R´enyi divergence measure and our descriptor is proposed in order to design an active contour model for texture segmentation. To get a global solution and efficient, fast algorithm, the optimization problem is redefined. The algorithm associated with this last optimization problem avoids local minimums and the run-time consuming compared to the level-set representation of our active contour model. In order to illustrate the performance of the technique, some results are presented showing the effectiveness and robustness of our approach.

Keyword : level set theory, texture segmentation, global minimization, differential geometry, Rényi divergence measure, shape optimization, partial differential equations

How to Cite
Idrissi, S. Y. (2022). An active contour model for texture image segmentation using Rényi divergence measure. Mathematical Modelling and Analysis, 27(3), 429–451. https://doi.org/10.3846/mma.2022.14060
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Aug 12, 2022
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