2011
DOI: 10.1007/s10470-011-9630-9
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The gray level aura matrices for textured image segmentation

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 … Show more

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Cited by 9 publications
(6 citation statements)
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“…The amount of neighboring pixels with the specified level is quantified by means of the aura measure. GLAMs are used for texture representation and synthesis [41,42], image retrieval [43,44], classification [45][46][47][48][49][50], and segmentation [51][52][53]. A generalization of GLAMs to the fuzzy framework has been proposed by Hammouche et al [39].…”
Section: Fuzzy Color Texture Featuresmentioning
confidence: 99%
“…The amount of neighboring pixels with the specified level is quantified by means of the aura measure. GLAMs are used for texture representation and synthesis [41,42], image retrieval [43,44], classification [45][46][47][48][49][50], and segmentation [51][52][53]. A generalization of GLAMs to the fuzzy framework has been proposed by Hammouche et al [39].…”
Section: Fuzzy Color Texture Featuresmentioning
confidence: 99%
“…To reduce the computational cost and improve the classification performance, the basic gray level aura matrix (BGLAM) was proposed [ 118 ]. This is the basis of the gray level aura matrix (GLAM) [ 119 ] developed to overcome the limitation of GLCM that it cannot contain information about the interaction between gray level sets in textures with large scale structure [ 120 ]. BGLAM is characterized by the co-occurrence probability distribution of gray levels in all possible displacement configurations and it has been actively used for wood identification [ 27 , 28 , 59 , 61 ].…”
Section: Conventional Machine Learningmentioning
confidence: 99%
“…The above measure is adopted by most authors because it generalizes GLCMs and is well suited to image synthesis or processing problems [6][7][8][9][10][11][12][13][14][15][16]26]. However, this measure does not actually evaluate the number of sites of S g ′ that are neighbors of any site of S g , i.e., the number of sites that belong to…”
Section: Cardinal Aura Measure and Aura Matrixmentioning
confidence: 99%
“…In practice, as the number q of gray levels is equal to 256, we set the number p of intervals to 4, 8, and 16. The largest descriptors have then the same size (p 2 = 256) as Local Binary Patterns (LBPs) that are one of the most efficient texture features [34].…”
Section: Number Of Fuzzy Numbersmentioning
confidence: 99%
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