The detection of dropwise condensation in the camera field is a complex problem that requires an interdisciplinary approach in order to build an accurate model for image formation. In the current work, we analyze the effect of the dropwise condensation at the microscopic level using geometrical optics and optical simulation. The results shows that pixels from a region within foggy image area will tend to move toward the centroid of this patch in the RGB space. We also observed a loss in high frequencies of the image patch. Based on these findings we developed a method to detect condensation from a single image using the local gradient magnitude as a texture feature combined with the dispersion of the pixel color information in the RGB space. This approach segments the image into a clear and foggy regions using a clustering algorithm (Expectation Maximization). The miss-classified patches are corrected in a second pass using the probability of the neighboring patches to be foggy.

We successfully tested our algorithm using data collected by a Fog-Lab that was designed and built to capture images in a control environment at different condensation levels, and we believe that our work is the first that addresses condensation detection from a single image at early stages.