Indexing Topological Numbers on Images by Transferring Chiral Magnetic Textures
Abstract: Topological analysis is widely adopted in various research fields to unveil intricate features and structural relationships implied in geometrical objects. Especially, in the fields of data analysis, exploring the topological properties of various images offers rich insights into the intrinsic geometrical information within them. In this study, a novel approach is proposed to investigate the topological properties of arbitrary grayscale images by employing a straightforward procedure used in 2D magnetism studies to calculate topological numbers. This method utilizes machine learning techniques to transfer chiral magnetic textures onto the images. Then, the topological number is then computed directly from the converted images by integrating the solid angles formed by adjacent spin vectors. The method successfully identifies the topological numbers of various grayscale images, showing stable performances against small noises. Furthermore, two applications of the method: are demonstrated topological analysis of the Modified National Institute of Standards and Technology (MNIST) dataset and the counting of blood cells in microscopic images.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Indexing Topological Numbers on Images by Transferring Chiral Magnetic Textures ; day:25 ; month:06 ; year:2024 ; extent:8
Advanced Materials Technologies ; (25.06.2024) (gesamt 8)
- Urheber
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Park, Seong Min
Moon, Tea Jung
Yoon, Han Gyu
Kwon, Hee Young
Won, Changyeon
- DOI
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10.1002/admt.202400172
- URN
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urn:nbn:de:101:1-2406251430359.169859346090
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:56 MESZ
Datenpartner
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Beteiligte
- Park, Seong Min
- Moon, Tea Jung
- Yoon, Han Gyu
- Kwon, Hee Young
- Won, Changyeon
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