Detection of Islands and Droplets on Smectic Films Using Machine Learning

TL;DR

This paper presents a machine learning approach using SVMs to accurately detect islands and droplets on smectic liquid crystal films, outperforming traditional methods especially in complex imaging conditions.

Contribution

The study introduces a novel machine learning pipeline combining Canny edge detection and SVM classification for identifying inclusions on smectic films, improving detection accuracy in challenging images.

Findings

SVM achieves higher accuracy than conventional tracking software.

Method effectively detects objects in non-uniform backgrounds.

Applicable to biological and soft matter imaging environments.

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