  Imaging techniques are essential tools for inquiring a number of properties
from different materials. Liquid crystals are often investigated via optical
and image processing methods. In spite of that, considerably less attention has
been paid to the problem of extracting physical properties of liquid crystals
directly from textures images of these materials. Here we present an approach
that combines two physics-inspired image quantifiers (permutation entropy and
statistical complexity) with machine learning techniques for extracting
physical properties of nematic and cholesteric liquid crystals directly from
their textures images. We demonstrate the usefulness and accuracy of our
approach in a series of applications involving simulated and experimental
textures, in which physical properties of these materials (namely: average
order parameter, sample temperature, and cholesteric pitch length) are
predicted with significant precision. Finally, we believe our approach can be
useful in more complex liquid crystal experiments as well as for probing
physical properties of other materials that are investigated via imaging
techniques.
