Detection of Islands and Droplets on Smectic Films Using Machine Learning

Abstract

Machine learning techniques have been developed to identify inclusions on the surface of freely suspended smectic liquid crystal films imaged by reflected light microscopy. The experimental images are preprocessed using Canny edge detection and then passed to a radial kernel support vector machine (SVM) trained to recognize circular islands and droplets. The SVM is able to identify these objects of interest with an accuracy that far exceeds that of conventional tracking software, especially when the background image is non-uniform or when the target features are in close proximity to one another. This method could be applied to tracking objects in a variety of visually inhomogeneous biological and soft matter environments, in order to study growth dynamics, the development of spatial order, and hydrodynamic behavio

arXiv comment13 pages,1 figures, 1 table

References in corpus (7)

• Machine Learning for Fluid Mechanics
• Identifying structural flow defects in disordered solids using machine learning methods
• Particle diffusion in active fluids is non-monotonic in size
• Mutual diffusion of inclusions in freely-suspended smectic liquid crystal films
• Realization of hydrodynamic experiments on quasi-2D liquid crystal films in microgravity
• Annihilation of Point Defect Pairs in Freely Suspended Liquid-Crystal Films
• Experimental Realization of an Incompressible Newtonian Fluid in Two Dimensions

Cited by in corpus (4)

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• Machine learning topological defects in confluent tissues
• Interpretable liquid crystal phase classification via two-by-two ordinal patterns