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. A method is devised to identify the topological numbers of grayscale images using simple neural networks and a straightforward procedure of skyrmion number calculation. The method shows a stable performance against small noise and defects, compared to a traditional method. Furthermore, two applications of the method: topological analysis of the Modified National Institute of Standards and Technology (MNIST) data and the counting of blood cells in microscopic images are demonstrated. image