In this paper, we introduce a method to visually inspect industrial parts by using multi-view images of an industrial part on a rotating table. During the visual inspection of the parts' surfaces, the relationship between the camera pose, the light vectors, and the normal vectors of a part's surface is the key factor in detecting abnormalities. We can change the relationship between the three factors by rotating the part; then, the abnormalities are visible in certain positional relationships. We, therefore, track the points on a part's surfaces in an image sequence and discriminate abnormal points (such as points on scratches or dents) from normal points based on pixel value transitions while rotating the part. The experimental results, which were based on real data of industrial parts, showed that the proposed method could detect the abnormalities on the surfaces.
In the field of Automated Optical Inspection (AOI), measuring the 3D profile of objects can be of paramount importance, enabling the measure of surface roughness, critical small or large scale dimensions in x, y and z, radius of curvature; all of which requiring a high level of accuracy and repeatability. This paper presents a depth from focus surface profile recovery approach using a tunable lens. Two prototype systems and calibrations steps are detailed, several focus measures are introduced and implemented. Experimental results shows the applicability of the approach to small scale depth measurements and surface reconstruction.