  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
