Автоматизована побудова цифрової моделі мікроповерхні об'єкта за РЕМ-стереопарою методом кореляційного ототожнення ідентичних ділянок

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dc.citation.epage64
dc.citation.issue90
dc.citation.journalTitleГеодезія, картографія і аерофотознімання
dc.citation.spage50
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorІванчук, О. М.
dc.contributor.authorТумська, О. В.
dc.contributor.authorIvanchuk, O.
dc.contributor.authorTumska, O.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-02-13T10:51:25Z
dc.date.available2023-02-13T10:51:25Z
dc.date.created2019-03-12
dc.date.issued2019-03-12
dc.description.abstractМета роботи – розробити і дослідити метод автоматизованої побудови цифрової моделі мікроповерхні об’єкта з використанням стереопари цифрових РEM-зображень з урахуванням специфіки РEM-знімання і оцінки точності цифрового моделювання. Розроблений метод полягає, по-перше, у генеруванні щільного набору вхідних точок на лівому РEM-зображенні стереопари в областях з локальними особливостями і використанні ітераційного процесу по рівнях піраміди зображень. По-друге, пошук відповідних точок на правому РEM-зображенні стереопари виконується на основі послідовного зміщення точок (центрів вікон пошуку) на параметр зсуву з можливого діапазону паралаксів із використанням методу кореляційного ототожнення. Для дослідження використано дві стереопари цифрових РEM-зображень. Цифрові зображення деформованої поверхні хромованої сталі отримано за допомогою JSM 7100F (JEOL) зі збільшенням 750х. Зображення лесового ґрунту отримано за допомогою РЕМ “Hitachi” S-800 зі збільшенням 1000х. Під час розрахунку просторових координат точок мікрорельєфу поверхні враховано значення геометричних спотворень, властивих РЕМ-знімку. Щоб усунути деякі аномальні значення висот тривимірної моделі, застосовано процедуру адаптивної медіанної фільтрації. Для оцінювання точності моделювання мікроповерхонь були створені тестові моделі шляхом ручного вимірювання координат характерних точок цифрових стереопар обох зразків. Заропоновано спосіб зсуву параметрів, який зменшує пошук і ймовірність помилкової ідентифікації і, крім того, прискорює процедуру ототожнення в парі зображень. Отримано формули для розрахунку координат центру вікна пошуку та відповідної точки на правому зображенні на k-му кроці процесу зсуву. Для оцінювання точності обчислені різниці між висотами тестової моделі і висотами, інтерпольованими в тих самих точках з використанням створених моделей. Для мікроповерхні зразка хромованої сталі близько 79 % точок, а для мікроповерхні зразка лесового ґрунту близько 70 % точок містяться в межах допуску ΔZ ≤ ± 2 мкм. Вперше в Україні розроблено метод автоматизованого пошуку відповідних точок стереопари на основі зсуву параметрів з урахуванням особливостей РЕМ-знімання. На основі вищевказаного методу розроблено технологію автоматизованого створення цифрової моделі мікроповерхні об’єкта за стереопарою РEM-зображень і створено авторське програмне забезпечення, яке показує її ефективність і доцільність. Можливість відтворювати мікрорельєф поверхні об’єкта автоматизовано з використанням стереопари цифрових РEM-зображень відповідно до вимог точності визначення просторових координат точок та структури мікроповерхні об’єкта.
dc.description.abstractPurpose. The goal of this work was the development and research of a method of automatically constructing a digital model of the micro surface of an object from SEM stereo pair of digital images taking into account the specifics of the survey SEM and evaluating the accuracy of digital modeling. Methods. The developed method consists, firstly, in generating a dense set of input points in the left SEM image of a stereo pair in regions with local features and using an iterative process in accordance with the levels of the image pyramid. Secondly, the search for the corresponding points in the right SEM image of the stereo pair is carried out on the basis of sequentially shifting the points (centers of the search windows) by a shift parameter from the possible parallax's range using the correlation method. For research, we have used two stereo pairs of digital SEM images. Digital images of the deformed surface chrome steel specimen were acquired with the JSM 7100F (JEOL) with magnification 750x. Images of loess soil were acquired with the SEM Hitachi” S-800 with magnification 1000x. When calculating the spatial coordinates of the points of the surface micro relief, the values of geometric distortion inherent in the SEM image were taken into account. To eliminate some anomalous values of the heights of the 3D model, an adaptive median filtering procedure was applied. To evaluate the accuracy of micro surface simulation test models were created by manually measuring coordinate feature points of the digital stereo pairs for both specimens. Results. The proposed method for shifting parameters reduces the search area and the probability of mismatch and, in addition, speeds up the matching procedure in a pair of images. Formulas are obtained for calculating the coordinates of the center of the search window and the corresponding point in the right image at the k-th step of the shift process. To estimate the accuracy, the differences between the heights of the test model and the heights interpolated at the same points using the created models were computed. For the chrome steel specimen micro surface about 79 % of the points, and for the micro surface specimen of the loess soil about 70 % of the points were within tolerance ΔZ ≤ ± 2 μm. Scientific novelty. For the first time in Ukraine, a method was developed for an automatic search of corresponding points based on a shift of parameters taking into account the features of SEM survey. The proposed technological reconstruction automation scheme of a digital model of an object’s micro surface from SEM stereo pair, and the creation of this authoring software show its efficiency and expediency. The practical significance. The ability to reproduce the surface micro relief of an object automatically using a stereo pair of SEM digital images was established in accordance with the requirements of both the accuracy of determining the spatial coordinates of points and the structure of the micro surface of the object.
dc.format.extent50-64
dc.format.pages15
dc.identifier.citationІванчук О. М. Автоматизована побудова цифрової моделі мікроповерхні об'єкта за РЕМ-стереопарою методом кореляційного ототожнення ідентичних ділянок / О. М. Іванчук, О. В. Тумська // Геодезія, картографія і аерофотознімання. — Львів : Видавництво Львівської політехніки, 2019. — № 90. — С. 50–64.
dc.identifier.citationenIvanchuk O. Automated generation of a digital model of an object's micro surface from a SEM-stereo pair by area-based image matching / O. Ivanchuk, O. Tumska // Geodesy, cartography and aerial photography. — Lviv : Lviv Politechnic Publishing House, 2019. — No 90. — P. 50–64.
dc.identifier.doidoi.org/10.23939/istcgcap2019.90.050
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/57349
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofГеодезія, картографія і аерофотознімання, 90, 2019
dc.relation.ispartofGeodesy, cartography and aerial photography, 90, 2019
dc.relation.referencesBaghaie, A., Tafti, A. P., Owen, H. A., D’Souza, R. M., & Yu, Z. (2017). Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation.
dc.relation.referencesPloS one, 12(4), e0175078.
dc.relation.referencesBałamucki, J., Czarnecki, P., Gotszalk, T., Marendziak, A., Rangelow, I., Wilk, J., & Kowalski, Z. W. (2006). Profilometric, SEM and AFM Investigations of Titanium and Steel Surface Micro-and Nanoroughness Induced by
dc.relation.referencesNeutralized Krypton Ion Beam as a First Stage of Fractal Analysis. Physics and Chemistry of Solid State, 7(3).
dc.relation.referencesCornille, N., Garcia, D., Sutton, M. A., McNeill, S., &
dc.relation.referencesOrteu, J. J. (2003, June). Automated 3D reconstruction using a scanning electron microscope. In SEM conference on experimental and applied mechanics (pp. 2–4). Charlotte. Dorozhynskyy, O. L, & Tukay, R. (2008).
dc.relation.referencesPhotogrammetry: A textbook. Lviv Polytechnic Publishing House, 332 p.
dc.relation.referencesGonzales, R., & Woods, R. (2005). Digital Image Processing. M., Tehnosfera (in Russian).
dc.relation.referencesGonzales, R., Vuds, R., & Eddins, S. (2006). Digital Image Processing using MATLAB. M.: Tekhnosfera(in Russian).
dc.relation.referencesGorbachev, V. A. (2014). Development of algorithms for highly detailed object modeling based on digital image analysis: dis. for the degree of candidate phys.-mat. of science. MPhTI (SU). Moscow (in Russian).
dc.relation.referencesGruber M., & Leber F. (2000). High Quality Photogrammetric Scanning for Mapping. In China International Geoinformatics Industry, Technology and Equipment Exhibition. pp. 1–15.
dc.relation.referencesIvanchuk, O. M., & Khrupin, I. V. (2012). Structure and function of the program complex “Dimicros”processing of SEM images on a digital photogrammetric station. Modern achievements in geodetic science and
dc.relation.referencesindustry, 1(23), 193–197 (in Ukrainian).
dc.relation.referencesIvanchuk, O. (2015). Features Calibration geometric distortion of digital SEM images obtained at different SEM. Modern achievements in geodetic science and industry, I (29), 168–173 (in Ukrainian).
dc.relation.referencesIvanchuk, O. (2015). Research geometric distortion digital SEM-images obtained on SEM JSM-7100F (JEOL, Japan) and the accuracy of approximation. Geodesy, Cartography and Aerial photography, 81, 101–109. DOI:
dc.relation.referenceshttps://doi.org/10.23939/istcgcap2015.01.112
dc.relation.referencesIvanchuk, O. (2016). Mathematical model of the relationship of spatial coordinatesof points of a micro surface of a research object with their corresponding coordinates on SEM-stereo images. Modern achievements in
dc.relation.referencesgeodetic science and industry. I (31), 122–126.
dc.relation.referencesIvanchuk, O., & Tumska, O. (2016). Development and research of technology for automation of the calibration and account of digital SEM images having geometric distortion obtained with JCM – 5000 (Neoscope) (JEOL,
dc.relation.referencesJapan). Geodesy, cartography and aerial photography, 84, 56–64. DOI: https://doi.org/10.23939/istcgcap2016.02.056
dc.relation.referencesIvanchuk, O. M. (2017). Technology of processing of digital SEM images of solids micro surfaces., Urban planning and territorial planning: scientifictechnical. Sat. Kyiv: KNUBA, 63, 170–184 (in Ukrainian).
dc.relation.referencesIvanchuk, O., & Tumska, O. (2017). Method of automated determination of coordinates of centers of test object nodes for its SEM-images using MatLab tools. Modern achievements in geodetic science and industry, I
dc.relation.references(33), 158–165 (in Ukrainian).
dc.relation.referencesIvanchuk, O., & Tumska, O. (2017). A study of fractal and metric properties of images based on measurements data of multiscale digital SEM images of a test object obtained with different types of SEM, Geodesy,
dc.relation.referencescartography and aerial photography, 85, 53–64. DOI: https://doi.org/10.23939/istcgcap 2017.01.053
dc.relation.referencesIvanchuk, O. M. (2019). Theoretical and methodological foundations of spatial simulation of micro surfaces of objects based on the data of digital SEM photogrammetry. Abstract of a doctoral dissertation, Lviv, 44 p. (in
dc.relation.referencesUkrainian).
dc.relation.referencesIvanchuk, O., & Tumska, O. (2019). Automated construction of digital model of the micro surface an object using a stereo pair of digital SEM images], Modern achievements in geodetic science and industry, IІ (38), 72–96 (in
dc.relation.referencesUkrainian).
dc.relation.referencesKrig, S. (2016). Interest point detector and feature descriptor survey. In Computer vision metrics (pp. 187–246). Springer, Cham.
dc.relation.referencesKudryavtsev, A. (2017). 3D Reconstruction in Scanning Electron Microscope: from image acquisition to dense point cloud (Doctoral dissertation, Bourgogne Franche-Comté).
dc.relation.referencesKudryavtsev, A. V., Dembélé, S., & Piat, N. (2017, July). Stereo-image rectification for dense 3D reconstruction in scanning electron microscope. In: 2017 International Conference on Manipulation, Automation and Robotics at
dc.relation.referencesSmall Scales (MARSS), Montreal, (pp. 1–6). IEEE.
dc.relation.referencesLowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91–110.
dc.relation.referencesMarturi, N., Dembélé, S., & Piat, N. (2013, August). Fast image drift compensation in scanning electron microscope using image registration. In 2013 IEEE International Conference on Automation Science and Engineering
dc.relation.references(CASE) (pp. 1–6). IEEE.
dc.relation.referencesMelnik, V. M., Voloshyn, V. U., Tarasyuk, F. P., & Blinder Ju. S. (1999). Methods of quantitative characterization of soil microstructure. Bulletin of Lviv State University. Geographic series, 25, 24–27.
dc.relation.referencesIvan Franko National University of Lviv (in Ukrainian).
dc.relation.referencesMelnik, V. M., & Shostak, A. M. (2009). Raster electron stereomikrofraktografition, Luck, Vezha, 469 p. (in Ukrainian).
dc.relation.referencesMelnik, V. М., Radzij, V. F., Melnik, Ju. A. (2010). SEM analysis of the microstructure of sod-podzolic soils. Bulletin of geodesy and cartography. 5, 2–34 (in Ukrainian).
dc.relation.referencesMelnik, V., Blinder, Ju., & Piskunova, O. (2015).Methodology of radionuclide migration studies in soil cover. Modern achievements in geodetic science and industry, ІІ (30), 56–60.
dc.relation.referencesMelnik, Ju. A. (2013). Determination of structure and micro topography of characteristic surfaces of materials by 3D reconstruction method: author's abstract. dis. for the sciences degree candidate techn. sciences. 20 p.
dc.relation.referencesMikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors, IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 10, 1615–1630.
dc.relation.referencesNicolls, F. (2004, November). Structure and motion from SEM: a case study. In Fifteenth Annual Symposium of the Pattern Recognition Association of South Africa(p. 19).
dc.relation.referencesOtsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66.
dc.relation.referencesPopielski, P., & Wróbel, Z. (2012). The feature detection on the homogeneous surfaces with projected pattern. In Information Technologies in Biomedicine (pp. 118–128). Springer, Berlin, Heidelberg.
dc.relation.referencesSalahat, E., & Qasaimeh, M. (2017, March). Recent advances in features extraction and description algorithms: A comprehensive survey. In 2017 IEEE international conference on industrial technology (ICIT) (pp. 1059–1063).
dc.relation.referencesIEEE.
dc.relation.referencesShostak, A. V. (2012). Methods and models of microphotogrammetry in applied scientific research: abstract. diss. for the sciences degree doct. tech. sciences, Kyiv. 28 p. (in Ukrainian).
dc.relation.referencesSuprun, D. E. (2016). Algorithm for matching images by key points for scalability and rotation of objects.
dc.relation.referencesVestnik MGTU im. N.E. Bauman. Ser. Instrument making, 5, 86–98 (in Russian).
dc.relation.referencesTafti, A. P. (2016). 3D SEM surface reconstruction: An optimized, adaptive, and intelligent approach. PhD thesis, 1–140.
dc.relation.referencesVizilter, Ju. V., Zheltov, S. J., Bondarenko, A. V., Ososkov, M. V., & Morzhyn, A. V. (2010). Image processing and analysis in computer vision problems. Course of lectures and practical classes. Moscow: Phizmatkniga, 672 p.
dc.relation.references(in Russian).
dc.relation.referencesVoloshyn, V. U. (2004). Development of methods of SEM-photogrammetry and morphologic-fractal analysis (on example of bone fabric researching): author's abstract. dis. for the sciences degree candidate techn.
dc.relation.referencessciences], Lviv, 21 p. (in Ukrainian).
dc.relation.referencesZhu, T., Sutton, M. A., Li, N., Orteu, J. J., Cornille, N., Li, X., & Reynolds, A. P. (2011). Quantitative stereovision in a scanning electron microscope. Experimental Mechanics, 51(1), 97–109.
dc.relation.referencesenBaghaie, A., Tafti, A. P., Owen, H. A., D’Souza, R. M., & Yu, Z. (2017). Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation.
dc.relation.referencesenPloS one, 12(4), e0175078.
dc.relation.referencesenBałamucki, J., Czarnecki, P., Gotszalk, T., Marendziak, A., Rangelow, I., Wilk, J., & Kowalski, Z. W. (2006). Profilometric, SEM and AFM Investigations of Titanium and Steel Surface Micro-and Nanoroughness Induced by
dc.relation.referencesenNeutralized Krypton Ion Beam as a First Stage of Fractal Analysis. Physics and Chemistry of Solid State, 7(3).
dc.relation.referencesenCornille, N., Garcia, D., Sutton, M. A., McNeill, S., &
dc.relation.referencesenOrteu, J. J. (2003, June). Automated 3D reconstruction using a scanning electron microscope. In SEM conference on experimental and applied mechanics (pp. 2–4). Charlotte. Dorozhynskyy, O. L, & Tukay, R. (2008).
dc.relation.referencesenPhotogrammetry: A textbook. Lviv Polytechnic Publishing House, 332 p.
dc.relation.referencesenGonzales, R., & Woods, R. (2005). Digital Image Processing. M., Tehnosfera (in Russian).
dc.relation.referencesenGonzales, R., Vuds, R., & Eddins, S. (2006). Digital Image Processing using MATLAB. M., Tekhnosfera(in Russian).
dc.relation.referencesenGorbachev, V. A. (2014). Development of algorithms for highly detailed object modeling based on digital image analysis: dis. for the degree of candidate phys.-mat. of science. MPhTI (SU). Moscow (in Russian).
dc.relation.referencesenGruber M., & Leber F. (2000). High Quality Photogrammetric Scanning for Mapping. In China International Geoinformatics Industry, Technology and Equipment Exhibition. pp. 1–15.
dc.relation.referencesenIvanchuk, O. M., & Khrupin, I. V. (2012). Structure and function of the program complex "Dimicros"processing of SEM images on a digital photogrammetric station. Modern achievements in geodetic science and
dc.relation.referencesenindustry, 1(23), 193–197 (in Ukrainian).
dc.relation.referencesenIvanchuk, O. (2015). Features Calibration geometric distortion of digital SEM images obtained at different SEM. Modern achievements in geodetic science and industry, I (29), 168–173 (in Ukrainian).
dc.relation.referencesenIvanchuk, O. (2015). Research geometric distortion digital SEM-images obtained on SEM JSM-7100F (JEOL, Japan) and the accuracy of approximation. Geodesy, Cartography and Aerial photography, 81, 101–109. DOI:
dc.relation.referencesenhttps://doi.org/10.23939/istcgcap2015.01.112
dc.relation.referencesenIvanchuk, O. (2016). Mathematical model of the relationship of spatial coordinatesof points of a micro surface of a research object with their corresponding coordinates on SEM-stereo images. Modern achievements in
dc.relation.referencesengeodetic science and industry. I (31), 122–126.
dc.relation.referencesenIvanchuk, O., & Tumska, O. (2016). Development and research of technology for automation of the calibration and account of digital SEM images having geometric distortion obtained with JCM – 5000 (Neoscope) (JEOL,
dc.relation.referencesenJapan). Geodesy, cartography and aerial photography, 84, 56–64. DOI: https://doi.org/10.23939/istcgcap2016.02.056
dc.relation.referencesenIvanchuk, O. M. (2017). Technology of processing of digital SEM images of solids micro surfaces., Urban planning and territorial planning: scientifictechnical. Sat. Kyiv: KNUBA, 63, 170–184 (in Ukrainian).
dc.relation.referencesenIvanchuk, O., & Tumska, O. (2017). Method of automated determination of coordinates of centers of test object nodes for its SEM-images using MatLab tools. Modern achievements in geodetic science and industry, I
dc.relation.referencesen(33), 158–165 (in Ukrainian).
dc.relation.referencesenIvanchuk, O., & Tumska, O. (2017). A study of fractal and metric properties of images based on measurements data of multiscale digital SEM images of a test object obtained with different types of SEM, Geodesy,
dc.relation.referencesencartography and aerial photography, 85, 53–64. DOI: https://doi.org/10.23939/istcgcap 2017.01.053
dc.relation.referencesenIvanchuk, O. M. (2019). Theoretical and methodological foundations of spatial simulation of micro surfaces of objects based on the data of digital SEM photogrammetry. Abstract of a doctoral dissertation, Lviv, 44 p. (in
dc.relation.referencesenUkrainian).
dc.relation.referencesenIvanchuk, O., & Tumska, O. (2019). Automated construction of digital model of the micro surface an object using a stereo pair of digital SEM images], Modern achievements in geodetic science and industry, II (38), 72–96 (in
dc.relation.referencesenUkrainian).
dc.relation.referencesenKrig, S. (2016). Interest point detector and feature descriptor survey. In Computer vision metrics (pp. 187–246). Springer, Cham.
dc.relation.referencesenKudryavtsev, A. (2017). 3D Reconstruction in Scanning Electron Microscope: from image acquisition to dense point cloud (Doctoral dissertation, Bourgogne Franche-Comté).
dc.relation.referencesenKudryavtsev, A. V., Dembélé, S., & Piat, N. (2017, July). Stereo-image rectification for dense 3D reconstruction in scanning electron microscope. In: 2017 International Conference on Manipulation, Automation and Robotics at
dc.relation.referencesenSmall Scales (MARSS), Montreal, (pp. 1–6). IEEE.
dc.relation.referencesenLowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91–110.
dc.relation.referencesenMarturi, N., Dembélé, S., & Piat, N. (2013, August). Fast image drift compensation in scanning electron microscope using image registration. In 2013 IEEE International Conference on Automation Science and Engineering
dc.relation.referencesen(CASE) (pp. 1–6). IEEE.
dc.relation.referencesenMelnik, V. M., Voloshyn, V. U., Tarasyuk, F. P., & Blinder Ju. S. (1999). Methods of quantitative characterization of soil microstructure. Bulletin of Lviv State University. Geographic series, 25, 24–27.
dc.relation.referencesenIvan Franko National University of Lviv (in Ukrainian).
dc.relation.referencesenMelnik, V. M., & Shostak, A. M. (2009). Raster electron stereomikrofraktografition, Luck, Vezha, 469 p. (in Ukrainian).
dc.relation.referencesenMelnik, V. M., Radzij, V. F., Melnik, Ju. A. (2010). SEM analysis of the microstructure of sod-podzolic soils. Bulletin of geodesy and cartography. 5, 2–34 (in Ukrainian).
dc.relation.referencesenMelnik, V., Blinder, Ju., & Piskunova, O. (2015).Methodology of radionuclide migration studies in soil cover. Modern achievements in geodetic science and industry, II (30), 56–60.
dc.relation.referencesenMelnik, Ju. A. (2013). Determination of structure and micro topography of characteristic surfaces of materials by 3D reconstruction method: author's abstract. dis. for the sciences degree candidate techn. sciences. 20 p.
dc.relation.referencesenMikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors, IEEE Trans. Pattern Analysis and Machine Intelligence, 27, 10, 1615–1630.
dc.relation.referencesenNicolls, F. (2004, November). Structure and motion from SEM: a case study. In Fifteenth Annual Symposium of the Pattern Recognition Association of South Africa(p. 19).
dc.relation.referencesenOtsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66.
dc.relation.referencesenPopielski, P., & Wróbel, Z. (2012). The feature detection on the homogeneous surfaces with projected pattern. In Information Technologies in Biomedicine (pp. 118–128). Springer, Berlin, Heidelberg.
dc.relation.referencesenSalahat, E., & Qasaimeh, M. (2017, March). Recent advances in features extraction and description algorithms: A comprehensive survey. In 2017 IEEE international conference on industrial technology (ICIT) (pp. 1059–1063).
dc.relation.referencesenIEEE.
dc.relation.referencesenShostak, A. V. (2012). Methods and models of microphotogrammetry in applied scientific research: abstract. diss. for the sciences degree doct. tech. sciences, Kyiv. 28 p. (in Ukrainian).
dc.relation.referencesenSuprun, D. E. (2016). Algorithm for matching images by key points for scalability and rotation of objects.
dc.relation.referencesenVestnik MGTU im. N.E. Bauman. Ser. Instrument making, 5, 86–98 (in Russian).
dc.relation.referencesenTafti, A. P. (2016). 3D SEM surface reconstruction: An optimized, adaptive, and intelligent approach. PhD thesis, 1–140.
dc.relation.referencesenVizilter, Ju. V., Zheltov, S. J., Bondarenko, A. V., Ososkov, M. V., & Morzhyn, A. V. (2010). Image processing and analysis in computer vision problems. Course of lectures and practical classes. Moscow: Phizmatkniga, 672 p.
dc.relation.referencesen(in Russian).
dc.relation.referencesenVoloshyn, V. U. (2004). Development of methods of SEM-photogrammetry and morphologic-fractal analysis (on example of bone fabric researching): author's abstract. dis. for the sciences degree candidate techn.
dc.relation.referencesensciences], Lviv, 21 p. (in Ukrainian).
dc.relation.referencesenZhu, T., Sutton, M. A., Li, N., Orteu, J. J., Cornille, N., Li, X., & Reynolds, A. P. (2011). Quantitative stereovision in a scanning electron microscope. Experimental Mechanics, 51(1), 97–109.
dc.relation.urihttps://doi.org/10.23939/istcgcap2015.01.112
dc.relation.urihttps://doi.org/10.23939/istcgcap2016.02.056
dc.relation.urihttps://doi.org/10.23939/istcgcap
dc.rights.holder© Національний університет “Львівська політехніка”, 2019
dc.subjectРЕМ-стереопара
dc.subjectкореляційне ототожнення
dc.subjectточність
dc.subject3D-модель
dc.subjectscanning electron microscope (SEM)
dc.subjectSEM stereo pair
dc.subjectimage matching
dc.subjectaccuracy
dc.subject3D model
dc.subject.udc528.721.287
dc.subject.udc537.533.35
dc.titleАвтоматизована побудова цифрової моделі мікроповерхні об'єкта за РЕМ-стереопарою методом кореляційного ототожнення ідентичних ділянок
dc.title.alternativeAutomated generation of a digital model of an object's micro surface from a SEM-stereo pair by area-based image matching
dc.typeArticle

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Геодезія, картографія і аерофотознімання. – 2019. – Випуск 90