Unsupervised colour image segmentation applied to printing quality assessment

Authors:
L. Bergman;A. Verikas;M. Bacauskiene
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden;Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031 Kaunas, Lithuan ...;Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031 Kaunas, Lithuania
Venue:
Image and Vision Computing
Year:
2005

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Abstract

We present an option for colour image segmentation applied to printing quality assessment in offset lithographic printing by measuring an average ink dot size in halftone pictures. The segmentation is accomplished in two stages through classification of image pixels. In the first stage, rough image segmentation is performed. The results of the first segmentation stage are then utilized to collect a balanced training data set for learning refined parameters of the decision rules. The developed software is successfully used in a printing shop to assess the ink dot size on paper and printing plates.