Texture Analysis of Magnetic Domain Images Using Statistics Based on Human Visual Perception
Ryo Murakami, Masaichiro Mizumaki, Yusuke Hamano, Ichiro Akai, Hayaru Shouno · Journal of the Physical Society of Japan · 2021
In magnetic materials development, interpreting patterns of image data and estimating physical properties from image data are important. Specifically, magnetic domain images reflect the performance of magnetic materials. However, magnetic domain images are often evaluated qualitatively, i.e., they have a maze structure or an island structure. Therefore, this study quantitatively investigates the features describing the patterns of magnetic domain images, based on the Portilla–Simoncelli texture statistics (PSS). PSS is based on human visual perception, and is a strong tool to quantify texture structures. In the texture analysis of magnetic domain images, we primarily investigated the features describing texture structures, i.e., maze or island structures. In a secondary investigation, we estimated the physical properties using the texture statistics obtained from magnetic domain images. We determined the metrics of patterns from the magnetic domain images using PSS. Furthermore, we demonstrated that PSS can robustly estimate physical properties from magnetic domain images.