Vision feature fusion for classification of electrical motors in an industrial recycling process

Abstract

This paper describes the fusion process of image features from vision data in an industrial system for disassembly of worn out electrical motors. In the fusion process fuzzy measures are used to fuse Gaussian shaped possibility measures. After training, the system is able to correctly classify incoming motors to a degree of 95%.