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The gray level aura matrices for textured image segmentation

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Abstract

Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.

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  1. Laboratoire Robotique et Vision, Département d’Automatique, Faculté de Génie Electrique et d’Informatique, Université Mouloud Mammeri, Tizi-Ouzou, Algérie

    Zohra Haliche & Kamal Hammouche

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  1. Zohra Haliche
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  2. Kamal Hammouche
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Correspondence to Kamal Hammouche.

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Haliche, Z., Hammouche, K. The gray level aura matrices for textured image segmentation. Analog Integr Circ Sig Process 69, 29–38 (2011). https://doi.org/10.1007/s10470-011-9630-9

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