Colour Image Segmentation Technique for Screen Printing

Abstract

Screen-printing is an industry with a large number of applications ranging from printing mobile phone logos to printing artworks on fabrics. In screen-printing, image segmentation plays a critical role in deciding both the cost and the quality attributes of printing. The work presented in this paper focuses on the development of an approach to colour image segmentation using a combined approach of k-means clustering and principal component analysis. The uncorrelated image data obtained through a principal component analysis was clustered using a k-means clustering algorithm. Since the study focused on the screen printing industry, the selection of the number of clusters k which is the most critical element is allowed to be set manually so that users can limit the number of colours to be segmented. It is shown that this approach produces a significant improvement in colour image segmentation. Results are also compared with another popular clustering algorithm called the mean-shift, which is normally used in feature space clustering.