Abstract
An efficient method for optimizing the parameters used for image processing is described that applies estimation error reduction to design of experiments (DOE). The traditional DOE optimization method is used to estimate the evaluation scores of all parameter sets and to rank them using a small number of actual scores. Because the search for the optimal parameter set is done in the order of the estimated scores for all parameter sets, the ranking accuracy, which strongly depends on the estimation error, is important. We introduce a function for reducing the estimation errors for the higher ranked parameter sets. The proposed parameter optimization method was evaluated by applying it to parameter optimization for industrial image defect area extraction. Evaluation using three datasets showed that the parameter sets selected by the proposed method had close to the highest actual score and that the number of image processings was 1/57 that of a full search procedure.
