Emulsion quality evaluation using machine vision
techniques depends on the efficiency of the image segmentation
algorithms. Two different machine vision techniques are
investigated to determine their competency in detecting droplets
from in-process microscopic images of a cream emulsion.
Histogram-based segmentation shows promising potential
compared to edge and symmetry detection. A statistical study of
the droplet characteristics was conducted. The results
demonstrate that the histogram-based approach is more
proficient in the progressive analysis of droplet evolution during
emulsification. A real-time integration of the technique is
proposed, as a soft sensor, to predict the optimum process time
and to increase manufacturing efficiency in chemical industries.