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Characterization of Magnetic Labyrinthine Structures through Junctions and Terminals Detection using Template Matching and CNN. (arXiv:2401.16688v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
In material sciences, characterizing faults in periodic structures is vital
for understanding material properties. To characterize magnetic labyrinthine
patterns, it is necessary to accurately identify junctions and terminals, often
featuring over a thousand closely packed defects per image. This study
introduces a new technique called TM-CNN (Template Matching - Convolutional
Neural Network) designed to detect a multitude of small objects in images, such
as defects in magnetic labyrinthine patterns. TM-CNN was used to identify these
structures in 444 experimental images, …
arxiv cnn cs.cv defects detection identify image material patterns per study template through understanding vital