Cork quality classification system using a unified image processing and fuzzy-neural network methodology

Cork is a natural material produced in the Mediterranean countries. Cork stoppers are used to seal wine bottles. Cork stopper quality classification is a practical pattern classification problem. The cork stoppers are grouped into eight classes according to the degree of defects on the cork surface. These defects appear in the form of random-shaped holes, cracks, and others. As a result, the classification cork stopper is not a simple object recognition problem. This is because the pattern features are not specifically defined to a particular shape or size. Thus, a complex classification form is involved. Furthermore, there is a need to build a standard quality control system in order to reduce the classification problems in the cork stopper industry. The solution requires factory automation meeting low time and reduced cost requirements. This paper describes a cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier. This approach will be used on a daily basis. A new adaptive image thresholding method using iterative and localized scheme is also proposed. A fully functioning prototype of the system has been built and successfully tested. The test results showed a 6.7% rejection ratio. It is compared with the 40% counterpart provided by traditional systems. The human experts in the cork stopper industry rated this proposed classification approach as excellent.