A study of methods for texture classification of SEM images of micro-surfaces of objects and their segmentation

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dc.citation.epage50
dc.citation.issue91
dc.citation.journalTitleГеодезія, картографія і аерофотознімання
dc.citation.spage41
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorІванчук, О.
dc.contributor.authorТумська, О.
dc.contributor.authorIvanchuk, O.
dc.contributor.authorTumska, O.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-03-02T08:01:39Z
dc.date.available2023-03-02T08:01:39Z
dc.date.created2020-03-12
dc.date.issued2020-03-12
dc.description.abstractМета. Мета роботи – розробити і дослідити методи класифікації текстур РЕМ-зображень мікроповерхонь об’єктів на основі статистичних та спектральних характеристик текстурних фрагментів, а також порівняльного аналізу методів сегментації РЕМ-зображень. Методи. Визначення характеристик текстури РEM-зображень ґрунтувалось на статистичних моментах, розрахованих за гістограмою яскравості. Спектральні міри текстури обчислювались за спектром Фур’є. Для визначення спектральних текстурних характеристик вибрано параметри амплітудної та осьової функцій. Сегментацію РEM-зображень мікроповерхонь об’єктів виконано чотирма способами, а саме: методом глобальної порогової сегментації, методом нарощування області, методом поділу та злиття і методом вододілу з використанням маркерів. Результати. Опрацювання серії РЕМ-зображень ґрунтів показало найкращий результат класифікації текстур за мірою однорідності, ніж за іншими статистичними характеристиками. Обчислення спектральних характеристик РЕМ-зображень металів виявило періодичність або майже періодичність і спрямованість присутніх у зображенні елементів текстур і разом із результатами класифікації за мірою однорідності дає змогу отримати узагальнену характеристику текстури зображення. Порівняльний аналіз чотирьох методів сегментації показав, що найкращий результат визначення меж об’єктів на РЕM-зображенні отримано методом вододілу з використанням маркерів. Програмну реалізацію методів класифікації текстур та їхню сегментацію виконували в системі MatLab. Наукова новизна. Запропоновано метод класифікації РЕМ-зображень на основі спектральних текстурних характеристик за параметрами амплітудної та осьової функцій. Показано, що сегментація РEM-зображень методом поділу і злиття дає змогу задати умови для виділення на зображенні областей із певними характеристиками текстури. Практичне значення. Узагальнена характеристика текстури РЕМ-зображення, що визначається за статистичними і спектральними мірами, корисна для автоматизованого розпізнавання текстур і аналізу РЕМ-зображень. Вибір ділянок із певними характеристиками текстури є важливим етапом попередньої обробки зображень під час знаходження точок інтересу, що придатні для зіставлення РЕМ-зображень і розпізнавання об’єктів.
dc.description.abstractPurpose. The goal of this work was to develop and study the methods of texture classification of SEM images of micro surfaces of objects based on the statistical and spectral characteristics of texture fragments, as well as a comparative analysis of segmentation methods of SEM images. Methods. The determination of the texture characteristics was based on statistical moments computed by the brightness histogram of a SEM-image or its region. The spectral measures of texture of SEM image were based on properties of the Fourier spectrum. To determine the spectral texture characteristics, the parameters of the amplitude and axial functions were chosen. SEM images were segmented using four methods, namely: the global thresholding; the region growing; the region splitting and merging; and the watershed using markers. Results. The experiments on texture classification of the SEM series of soils and metals images showed the best result of texture classification by the feature of homogeneity compared to other statistical characteristics. Calculation of the spectral characteristics was used to detect the directionality of periodic or almost periodic texture elements in the SEM images of metals. Classification results using spectral properties and homogeneity values made it possible to obtain generalized texture characteristics of SEM images of metals. A comparative analysis of the four segmentation methods showed that the best result of finding the boundaries of objects in the SEM image was obtained by the watershed method using markers. Software implementation of texture classification and image segmentation methods were performed in the MatLab system. Scientific novelty. The authors proposed a method for classifying SEM-images based on spectral texture characteristics using the parameters of the amplitude and axial functions. It is shown that the segmentation by the splitting and merging method allows you to set the conditions for selecting regions with certain texture characteristics in the SEM-image. The practical significance. A generalized characteristic of SEM-image texture, determined by statistical and spectral measurements, is that it would be useful for automatic texture recognition and SEM-images analysis. The selection of regions with certain texture characteristics is the preprocessing step for finding points of interest suitable for the SEM-image matching and objects recognition.
dc.format.extent41-50
dc.format.pages10
dc.identifier.citationIvanchuk O. A study of methods for texture classification of SEM images of micro-surfaces of objects and their segmentation / O. Ivanchuk, O. Tumska // Geodesy, cartography and aerial photography. — Lviv : Lviv Politechnic Publishing House, 2020. — No 91. — P. 41–50.
dc.identifier.citationenIvanchuk O. A study of methods for texture classification of SEM images of micro-surfaces of objects and their segmentation / O. Ivanchuk, O. Tumska // Geodesy, cartography and aerial photography. — Lviv : Lviv Politechnic Publishing House, 2020. — No 91. — P. 41–50.
dc.identifier.doidoi.org/10.23939/istcgcap2020.91.041
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/57446
dc.language.isoen
dc.publisherВидавництво Національного університету “Львівська політехніка”
dc.publisherLviv Politechnic Publishing House
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dc.relation.ispartofGeodesy, cartography and aerial photography, 91, 2020
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dc.relation.referencesenBagalkote, I. S., & Vibhute, A. S. (2016). Review on:
dc.relation.referencesenTexture Discrimination Feature Analysis for Visually
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dc.relation.referencesenBavrina, A. Ju., Ilyasova, N. Ju. (2002). Investigation of
dc.relation.referencesenphotogrammetric images using brightness distribution
dc.relation.referencesenprobality matrices, Computer Optics, 23, 62–65.
dc.relation.referencesenBhosle, V. V., Pawar, V. P. (2013). Texture Segmentation:
dc.relation.referencesenDifferent Methods. International Journal of Soft
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dc.relation.referencesenReview of Texture Classification Methods and
dc.relation.referencesenDatabases. 1–8. 10.1109/SIBGRAPI-T.2017.10.
dc.relation.referencesenCord, A., Bach, F., Jeulin, D. Texture classification by
dc.relation.referencesenstatistical learning from morphological image
dc.relation.referencesenprocessing: application to metallic surfaces. J. Microsc.239(2), 159–166 (2010).
dc.relation.referencesenFisenko, V. T., Fisenko, T. Yu. (2013). Fractal methods of
dc.relation.referencesentexture image segmentation], Instrument making, 56(5), 63–70.
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dc.relation.referencesenApproach, Moscow: Williams Publishing House, 2004, 928 p.
dc.relation.referencesenGolduyeva, D. A., Mokshanina, M. A. (2015). Trace
dc.relation.referencesentransformation as the basis of a method for segmenting
dc.relation.referencesenhalftone textures. Models, systems, networks in
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dc.relation.referencesenGonzalez, R. C., & Woods, R. E. (2005). Book on
dc.relation.referencesen"Digital image processing". Prentice-Hall of India
dc.relation.referencesenPvt. Ltd.
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dc.relation.referencesenUsing MATLAB. Publishing House of Electronics
dc.relation.referencesenIndustry, 76–77.
dc.relation.referencesenGonzales-Barron, U. A. & Butler, F. (2006). Statistical
dc.relation.referencesenand spectral texture analysis methods for discrimination of bread crumb images. pp. 749–759.
dc.relation.referencesenDOI: 10.1051/IUFoST:20060164.Gray, A, & Marshall S. & McKenzie, J. (2006). Modeling of
dc.relation.referencesenevolving textures using granulometries. Chapter in
dc.relation.referencesenEurasip Book Series on Signal Processing and
dc.relation.referencesenCommunications, pp. 240–270.
dc.relation.referencesenGulakov, V. K., Trubakov, A. O., S. N. Ogurtsov, S. N. (2011). Informative significance of texture characteristics
dc.relation.referencesenbased on the adjacency matrix of brightness levels of
dc.relation.referencesenimage pixels, Bulletin of the Bryansk State Technical
dc.relation.referencesenUniversity. 2(30), 81–85.
dc.relation.referencesenHaindl, M., Mikeš, S. (2016). Unsupervised Texture
dc.relation.referencesenSegmentation. In Pattern Recognition Techniques,
dc.relation.referencesenTechnology and Applications by Peng-Yeng Yin
dc.relation.referencesen(eds), Chap. 9. 227–248
dc.relation.referencesenHaralick, R. M. (1979). Statistical and structural approach to
dc.relation.referencesentextures, ProceedingsIEEE, 67, 786–804.
dc.relation.referencesenHu, X. (2017). Frequency Based Texture Featurte Descriptors. 147 p.
dc.relation.referencesenIvanchuk, O., Tumska, O. (2017). Comparative analysis
dc.relation.referencesenof the statistical and scaling characteristics of SEM
dc.relation.referencesenimages, obtained on different types of SEM, Recent
dc.relation.referencesenadvances in geodetic science and industry, Lviv, II (34), 119–131.
dc.relation.referencesenKhokhlov, M., Fischer, A., Rittel, D. (2012). Multi-Scale
dc.relation.referencesenStereo-Photogrammetry System for Fractographic.
dc.relation.referencesenAnalysis Using Scanning Electron Microscopy.
dc.relation.referencesenExperimental Mechanics. 52, 975–991. DOI 10.1007/s11340-011-9582-0.
dc.relation.referencesenKolodnikova, N. V. (2004). A review of texture features
dc.relation.referencesenfor pattern recognition tasks. Proceedings of the
dc.relation.referencesenemployees of TUSUR. Tomsk, 113–124.
dc.relation.referencesenKupriyanov, A. V. (2008). Segmentation of texture images
dc.relation.referencesenbased on the evaluation of local statistical signs,
dc.relation.referencesenBulletin of the Samara State Aerospace University,2, 245–251.
dc.relation.referencesenLee, J. H., Yoo, S. I. An effective image segmentation
dc.relation.referencesentechnique for the SEM image. Conference Paper May 2008, pp. 1–6. DOI: 10.1109/ICIT.2008.4608647,
dc.relation.referencesenSource: IEEE Xplore.
dc.relation.referencesenLiu, X. and Wang, D. L. Image and Texture Segmentation
dc.relation.referencesenUsing Local Spectral Histograms. IEEE transactions
dc.relation.referencesenon image processing, 15(10), 3066–3077.
dc.relation.referencesenLu, D. A., & Weng, Q. (2007). A survey of image
dc.relation.referencesenclassification methods and techniques for improving
dc.relation.referencesenclassification performance. International Journal of
dc.relation.referencesenRemote Sensing, 28(5), 823–870.
dc.relation.referencesenMadasu, V. K., Yarlagadda, P. (2007). An in depth
dc.relation.referencesencomparison of four texture segmentation methods.
dc.relation.referencesenDigital Image Computing Techniques and Applications.
dc.relation.referencesenIEEE. pp. 366-372. DOI 10.1109/DICTA.2007.83
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dc.rights.holder© Національний університет “Львівська політехніка”, 2020
dc.subjectрастровий електронний мікроскоп (РEM)
dc.subjectстатистичні та спектральні характеристики текстури РEM-зображення
dc.subjectкласифікація
dc.subjectсегментація
dc.subjectscanning electron microscope (SEM)
dc.subjectstatistical and spectral texture features of the SEM image
dc.subjectclassification
dc.subjectsegmentation
dc.subject.udc528.721.287
dc.subject.udc537.533.35
dc.titleA study of methods for texture classification of SEM images of micro-surfaces of objects and their segmentation
dc.title.alternativeДослідження методів класифікації текстур РЕМ-зображень мікроповерхонь об’єктів та їх сегментація
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Геодезія, картографія і аерофотознімання. – 2020. – Випуск 91