Applications of Inductive Learning to Automated Visual Inspection

Automated Visual Inspection: Wood Boards

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are based on texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cra...

Automated Visual Inspection of Road Surface Cracks

Pavement maintenance requires knowing the state of the road surface. Human inspection is the most common method for evaluating this state. Recently, the automated visual inspection has been addressed, but some important questions remain open concerning the variable ambient lighting, shadows, device synchronisation and the large amount of data. In the present paper, an automated visual inspectio...

Automated visual inspection of imprinted pharmaceutical tablets

This paper is on automated visual inspection of tablets that may, in contrast to manual tablet sorting, provide objective and reproducible tablet quality assurance. Visual inspection of the ever-increasing numbers of produced imprinted tablets, regulatory enforced for unambiguous identification of active ingredients and dosage strength of each tablet, is especially demanding. The problem become...

Corrosion Detection for Automated Visual Inspection

Image Fusion for Automated Visual Inspection

ion levels for image fusion The fusion of an image series can take place on different abstraction levels (Fig. 1): • Within a data-level fusion (pixel-level fusion), the combination takes place at the level of the image data, i.e. the gray or RGB values. The precondition of a data fusion is that the images have been recorded with homogeneous sensors.