Size from specular highlights for analyzing droplet size distributions

In mechanical engineering, heat-transfer models by dropwise condensation are under development. The condensation process is captured by taking many pictures, which show the formation of droplets, of which the size distribution and area coverage are of interest for model improvement. The current analysis method relies on manual measurements, which is time consuming. In this paper, we propose an approach to automatically extract the positions and radii of the droplets from an image. Our method relies on specular highlights that are visible on the surfaces of the droplets. We show that these highlights can be reliably extracted, and that they provide sufficient information to infer the droplet size. The results obtained by our method compare favorably with those obtained by laborious and careful manual measurements. The processing time per image is reduced by two orders of magnitude