000129640 001__ 129640
000129640 005__ 20241125101130.0
000129640 0247_ $$2doi$$a10.1002/crat.202200211
000129640 0248_ $$2sideral$$a132585
000129640 037__ $$aART-2023-132585
000129640 041__ $$aeng
000129640 100__ $$aBárcena-González, Guillermo
000129640 245__ $$aUnsupervised learning for the segmentation of small crystalline particles at the atomic level
000129640 260__ $$c2023
000129640 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129640 5203_ $$aElectron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D-STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration-corrected microscopy, offering new possibilities of analysis at nanometric scale.
000129640 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E13-20R$$9info:eu-repo/grantAgreement/EC/H2020/823717/EU/Enabling Science and Technology through European Electron Microscopy/ESTEEM3$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 823717-ESTEEM3$$9info:eu-repo/grantAgreement/ES/MCIU/RYC-2018-024561-I
000129640 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000129640 590__ $$a1.5$$b2023
000129640 592__ $$a0.346$$b2023
000129640 591__ $$aCRYSTALLOGRAPHY$$b18 / 33 = 0.545$$c2023$$dQ3$$eT2
000129640 593__ $$aChemistry (miscellaneous)$$c2023$$dQ3
000129640 593__ $$aMaterials Science (miscellaneous)$$c2023$$dQ3
000129640 593__ $$aCondensed Matter Physics$$c2023$$dQ3
000129640 594__ $$a2.5$$b2023
000129640 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000129640 700__ $$aHernández-Robles, Andrei
000129640 700__ $$0(orcid)0000-0002-5229-2717$$aMayoral, Álvaro$$uUniversidad de Zaragoza
000129640 700__ $$aMartinez, Lidia
000129640 700__ $$aHuttel, Yves
000129640 700__ $$aGalindo, Pedro L.
000129640 700__ $$aPonce, Arturo
000129640 7102_ $$12003$$2395$$aUniversidad de Zaragoza$$bDpto. Física Materia Condensa.$$cÁrea Física Materia Condensada
000129640 773__ $$g58, 3 (2023), 2200211 [8 pp.]$$pCryst. res. technol.$$tCRYSTAL RESEARCH AND TECHNOLOGY$$x0232-1300
000129640 8564_ $$s2267153$$uhttps://zaguan.unizar.es/record/129640/files/texto_completo.pdf$$yPostprint
000129640 8564_ $$s2068303$$uhttps://zaguan.unizar.es/record/129640/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000129640 909CO $$ooai:zaguan.unizar.es:129640$$particulos$$pdriver
000129640 951__ $$a2024-11-22-11:58:40
000129640 980__ $$aARTICLE