Results 31 to 40 of about 4,289,464 (313)
K-statistical operators for set mathematical morphology
Revista Facultad de Ingeniería Universidad de Antioquia, 2013 A generalization of the basic operators in mathematical morphology is presented in this paper. The resulting generalization lead to the construction of the k-statistical operators, due to its ability to obtain geometricaly the statistical descriptors ...
Juan Santiago Díaz de León +3 more
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MORPHOLOGICAL MODELING OF COLD SPRAY COATINGS
Image Analysis and Stereology, 2018 In this article, we study the microstructure of cold sprayed films of copper particles deposited onto a carbon fiber reinforced polymer. The microstructure of the coating is made of a packing of seemingly round-shaped particles of varying sizes embedded ...
Vincent Bortolussi +4 more
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ESTIMATION OF TORTUOSITY AND RECONSTRUCTION OF GEODESIC PATHS IN 3D
Image Analysis and Stereology, 2013 The morphological tortuosity of a geodesic path in a medium can be defined as the ratio between its geodesic length and the Euclidean distance between its two extremities.
Charles Peyrega, Dominique Jeulin
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MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ
Bioinform., 2016 MOTIVATION Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images.
D. Legland +2 more
semanticscholar +1 more source
A 2.5D APPROACH TO SKIN WRINKLES SEGMENTATION
Image Analysis and Stereology, 2019 Wrinkles or creases are common structures on surfaces. Their detection is often challenging, and can be an important step for many different applications. For instance, skin wrinkle segmentation is a crucial step for quantifying changes in skin wrinkling
Etienne Decencière +7 more
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Examples of Morphological Calculus [PDF]
, 2020 In this paper we present an introduction to morphological calculus in which geometrical objects play the rule of generalised natural numbers.
arxiv +1 more source
3D RECONSTRUCTION OF A MULTISCALE MICROSTRUCTURE BY ANISOTROPIC TESSELLATION MODELS
Image Analysis and Stereology, 2014 In the area of tessellation models, there is an intense activity to fully understand the classical models of Voronoi, Laguerre and Johnson-Mehl. Still, these models are all simulations of isotropic growth and are therefore limited to very simple and ...
Hellen Altendorf +4 more
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Learning Deep Morphological Networks with Neural Architecture Search [PDF]
Pattern Recognition, 108893 (2022), 2021 Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch
arxiv +1 more source
EIKONAL-BASED MODELS OF RANDOM TESSELLATIONS
Image Analysis and Stereology, 2019 In this article, we propose a novel, efficient method for computing a random tessellation from its vectorial representation at each voxel of a discretized domain.
Bruno Figliuzzi
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A multi-parameter persistence framework for mathematical morphology
Scientific Reports, 2022 The field of mathematical morphology offers well-studied techniques for image processing and is applicable for studies ranging from materials science to ecological pattern formation.
Yu-Min Chung, Sarah Day, Chuan-Shen Hu
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