Image Processing Application with a TSK Fuzzy Model
The authors have been involved in developing an automated inspection system, based on machine vision, to improve the repair coating quality control (RCQ control) in can ends of metal containers for fish food. The RCQ of each end is assesed estimating its average repair coating quality (ARCQ). In this work we present a fuzzy model building to make the acceptance/rejection decision for each can end from the information obtained by the vision system. In addition it is interesting to note that such model could be interpreted and supplemented by process operators. In order to achieve such aims, we use a fuzzy model due to its ability to favour the interpretability for many applications. Firstly, the easy open can end manufacturing process, and the current, conventional method for quality control of easy open can end repair coating, are described. Then, we show the machine vision system operations. After that, the fuzzy modeling, results obtained and their discussion are presented. Finally, concluding remarks are stated.
Unable to display preview. Download preview PDF.
- 1.CFIA. Metal can defects; identification and classification manual. Technical report, Canadian Food Inspection Agency, CFIA (1998)Google Scholar
- 2.Chiu, S.: Fuzzy model identification based on cluster estimation. J. of Intelligent & Fuzzy Systems 2(3), 267–278 (1994)Google Scholar
- 3.González, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
- 6.Jang, J.: Input selection for anfis learning. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1493–1499 (1996)Google Scholar
- 10.Lin, R., King, P., Johnston, M.: Examination of metal containers for integrity. In: Merker, R. (ed.) FDA’s Bacteriological Analytical Manual Online. Center for Food Safety and Applied Nutrition (CFSAN), U.S. Food & Drug Administration, FDA (1998)Google Scholar