mlsanityData to 14 Oct 2026

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation

arXiv 2509.11485 ↗First posted 15 Sep 2025cond-mat.mtrl-sci cs.CVcited by 0

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Abstract (arXiv)

Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization challenging. We investigate the evolution of such patterns in bismuth-doped yttrium iron garnet (Bi:YIG) films subjected to a magnetic field annealing protocol. A U-Net deep learning model, trained with synthetic degradations including additive white Gaussian and Simplex noise, enables robust segmentation of experimental magneto-optical images despite noise and occlusions. Building on this segmentation, we develop a geometric analysis pipeline based on skeletonization, graph mapping, and spline fitting, which quantifies local stripe propagation through length and curvature measurements. Applying this framework to 444 images from 12 annealing protocol trials, we analyze the transition from the "quenched" state to a more parallel and coherent "annealed" state, and identify two distinct evolution modes (Type A and Type B) linked to field polarity. Our results provide a quantitative analysis of geometric and topological properties in magnetic stripe patterns and offer new insights into their local structural evolution, and establish a general tool for analyzing complex labyrinthine systems.

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References · 17

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5 more
  1. 2023Image Restoration with Mean-Reverting Stochastic Differential Equationscited by 183
  2. 2023Diffusion Model for Generative Image Denoisingcited by 28
  3. 2016Robust LSTM-Autoencoders for Face De-Occlusion in the Wildcited by 17
  4. 2019Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditionscited by 6
  5. 2024Characterization of Magnetic Labyrinthine Structures Through Junctions and Terminals Detection Using Template Matching and CNNcited by 1

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