Image fusion techniques for robust inspection of specular surfaces

摘要

This contribution presents a new fusion strategy to inspect specular surfaces. To cope with illumination problems, several images are recorded with different lighting. Typically, the information of interest is extracted from each image separately and is then combined at a decision level. However, in our approach all images are processed simultaneously by means of a centralized fusion―no matter whether the desired results are images, features or symbols. Since the information fused is closer to the source, a better exploitation of the raw data is achieved. The sensors are virtual in the sense that a single camera is employed to record all images with different illumination patterns. The fusion problem is formulated by means of an energy function. Its minimization yields the desired fusion results, which describe surface defects. The performance of the proposed methodology is illustrated by means of two case studies: the analysis of machined surfaces, and the inspection of painted free-form surfaces. The programmable light sources utilized are a DMD, and an LED based illumination device, respectively. In both cases, the results demonstrate that by generating complementary imaging situations and using fusion techniques, a reliable yet cost-efficient inspection is attained matching the needs of industry.