A novel system for coloured object recognition

Abstract

Many industrial inspection tasks can be performed satisfactorily by conventional image processing systems which utilise feature extraction and related feature measuring algorithms. However, for the inspection of parameters such as texture or colour, these techniques may be inadequate. This paper outlines a new system which incorporates algorithms for coloured image processing and a logical neural network for subsequent pattern classification. Past research has indicated that logical neural nets can provide quite a good recognition performance for artefacts which are complex. However, in the area of coloured object inspection, the inspection tasks are more difficult. Furthermore, if the lighting source changes, the image of a coloured object will vary considerably and this will significantly affect the outputs of the networks. This paper presents a practical approach for colour invariant recognition and a method of adaptive sampling which utilises colour information more effectively. The system which is armed with these techniques provides a good recognition performance for artefacts which are both complex and coloured and, also, it is robust in terms of tolerance to noise and lighting.