Color mixing and random search for optimal illumination in machine vision

This study proposed how to find optimal illumination for industrial vision in short time using random search algorithm and multiple color light sources. The fineness of an image captured by a monochrome camera is varied by illumination and can be evaluated by image sharpness. The relation between the sharpness and the illumination is non-linear, so direct optimum methods are applicable to mix the multiple sources. Random search is one of the direct optimum methods and were derived from the sharpness as input and N driving voltages for N light sources. The random search was tested in an RGB mixer and reduced the number of iteration for optimal illumination compared with conventional equal step search.