ROBUST PARAMETER DESIGN OF DERIVATIVE OPTIMIZATION METHODS FOR IMAGE ACQUISITION USING A COLOR MIXER

摘要

A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of the industrial machine vision by adjusting multiple-color light emitting diodes (LEDs), usually called color mixers. Searching for the driving condition for achieving maximum sharpness, which influences image quality, using multiple color LEDs, is time-consuming. Hence, the steepest descent and conjugate gradient methods were applied to reduce the searching time for achieving maximum image quality. The relationship between lighting and image quality is multi-dimensional, non-linear, and difficult to describe using mathematical equations. Hence the Taguchi method is actually the only method that can determine the parameters of auto-lighting algorithms. The Taguchi method was applied to an inspection system consisting of an industrial camera, coaxial lens, color mixer, image acquisition device, analog interface board, and semiconductor patterns for target objects. The algorithm parameters were determined using orthogonal arrays and the candidate parameters were selected by increasing the sharpness and decreasing the iterations of the algorithm, which were dependent on the searching time. After conducting retests using the selected parameters, the image quality was almost the same as that in the best-case parameters with a smaller number of iterations. The Taguchi method will be useful in reducing time-consuming tasks and the time required to set up the inspection process in manufacturing.