An autonomous phase-boundary detection technique for colloidal hard sphere suspension experiments.
Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time-consuming. In addition, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The technique utilizes intelligent image processing algorithms that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application wherein regions of interest are distinguished from the background by differing patterns of motion over time.