Classification fusion by decision templates for insulating part control

This paper deals with insulating part quality control according to image analysis. The studied insulating parts are mainly composed of glass fibres and their orientation is directly correlated to the quality of the parts. This complex phenomenon is analyzed by means of 3D-tomographic images which give a huge set of raw data. Relevant features were extracted by several classification approaches to detect interesting regions defined by experts. The paper focuses on a fusion system based on decision templates to aggregate the previously obtained image classifications. The initial decision templates method proposed by L. Kuncheva is adapted for the image analysis concerned: (1) a solution is proposed to take into account classes of rejects for which there are no reference regions to learn the corresponding decision templates and (2) neighboring pixels are considered inside the fusion process of the decision templates. The fusion approach is then applied to part quality control. Results are assessed by means of the confusion matrix and accuracy measures show the great improvement in region detection brought by the fusion approach according to each input classification.