Description of images using a configuration equivalence relation

Myasnikov, V. M.

doi:10.18287/2412-6179-2018-42-6-998-1007

Cited by 8 publications

(3 citation statements)

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“…While the porosity of a thin film of SiNWs can be calculated from its optical density [6], it gives only the average porosity and it can be only applied for films with low light scattering. Different methods can be used for image processing [26][27][28]. In our work the porosity of the top of SiNW layer was estimated from an analysis of the corresponding top view SEM images.…”

Section: Porosity Evaluationmentioning

confidence: 99%

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

Zhanabaev

Grevtseva

Gonchar

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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This work has been done to identify quantitative criteria the degree of order and chaos morphology of porous layers consisting of silicon nanowire arrays. In order to fulfill the work, a method of using metal-assisted chemical etching has been utilized to produce nanowires. There has been done a work of digital processing of porous film images which were extracted by scanning electron microscope. Informational-entropic and Fourier analysis have been applied to quantitatively describe the degree of order and chaos in nanostructure distribution in the layers. Self-similarity of the layer morphology has been quantitatively described via its fractal dimensions by correlation method. The applied approach for image processing allows us to distinguish the morphological features of as-called "black" (more ordered) and "white" (less ordered) silicon layers, which are characterized by minimal and maximal optical reflection, respectively. From all of the methods of digital techniques that we have used the method for determining the conditional information of a chaotic set was proved to be the most informative.

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Section: Porosity Evaluationmentioning

confidence: 99%

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

Zhanabaev

Grevtseva

Gonchar

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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“…В различных задачах распознавания изображений со сложной текстурой часто используют локальные бинарные шаблоны (LBP) [14] -от анализа фотографий лиц людей [15] и построения описания изображений [16] до классификации гиперспектральных изображений нейронными сетями [17]. Метод позволяет добавить информацию об окружении каждого пикселя, сочетая пространственные и спектральные данные, и не обладает при этом вычислительной сложностью.…”

Section: Introductionunclassified

On classification of Sentinel-2 satellite images by a neural network ResNet-50

Bychkov¹,

Ruzhnikov²,

Fedorov³

et al. 2023

Computer Optics

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Various combinations of neural network parameters and sets of input data for satellite image classification are considered in the article. The training set is completed with a NDVI (normalized difference vegetation index) and local binary patterns. Testing of classifiers created on a different number of epochs and samples is carried out. Values of the neural network hyperparameters are determined that allow a classification accuracy of 0.70 and an F-measure of 0.65 to be achieved. Separation into classes with similar spectral characteristics is shown to offer low classification quality at different parameters and input data sets. Additional information is required. For example, for forests to be divided into more detailed classes, one needs to employ classifiers that use images from different seasons and vegetation periods. In addition, the training set needs to be extended to take into account various natural zones, soils, etc.

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“…The representation of images by random fields (RF) is generally accepted. To date, there is an extensive literature on image models (or RF models), for example, [1][2][3][4][5][6]. Much attention is paid to the modeling and processing of medical images for example, [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Double stochastic wave models of multidimensional random fields

Krasheninnikov

Subbotin

2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The paper deals with the development of mathematical models of random fields to describe and simulate images. In the wave model, a random field is the result of the influence of perturbations (waves) that occur at random times in random places and have random shapes. This model allows representing and simulate isotropic and anisotropic images (and their temporal sequences) defined on arbitrary areas of multidimensional space, as well as on any surfaces. The problems of correlation analysis and synthesis can be relatively easily solved. However, this model allows representing only homogeneous fields. In this paper, we consider «double stochastic» wave models, when the first wave random field (control field) sets the parameters of the second (controlled field). As a result, the controlled field becomes nonuniform, since its parameters vary randomly. We also consider options when two fields mutually influence each other. These models allow us to represent and simulate multidimensional inhomogeneous images (and their temporal sequences), as well as systems of such images with mutual correlations.

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Description of images using a configuration equivalence relation

Cited by 8 publications

References 0 publications

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

On classification of Sentinel-2 satellite images by a neural network ResNet-50

Double stochastic wave models of multidimensional random fields

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