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Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
2000 (English)In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 9, no 3, p. 227-242Article in journal (Refereed) Published
Abstract [en]

This paper presents a neural networks based method and a system for colour measurements on printed halftone multicoloured pictures and halftone multicoloured bars in newspapers. The measured values, called a colour vector, are used by the operator controlling the printing process to make appropriate ink feed adjustments to compensate for colour deviations of the picture being measured from the desired print. By the colour vector concept, we mean the CMY or CMYK (cyan, magenta, yellow and black) vector, which lives in the three- or four-dimensional space of printing inks. Two factors contribute to values of the vector components, namely the percentage of the area covered by cyan, magenta, yellow and black inks (tonal values) and ink densities. Values of the colour vector components increase if tonal values or ink densities rise, and vice versa. If some reference values of the colour vector components are set from a desired print, then after an appropriate calibration, the colour vector measured on an actual halftone multicoloured area directly shows how much the operator needs to raise or lower the cyan, magenta, yellow and black ink densities to compensate for colour deviation from the desired print. The 18 months experience of the use of the system in the printing shop witnesses its usefulness through the improved quality of multicoloured pictures, the reduced consumption of inks and, therefore, less severe problems of smearing and printing through.

Place, publisher, year, edition, pages
London: Springer, 2000. Vol. 9, no 3, p. 227-242
Keywords [en]
Colour classification, Colour printing, Decision fusion, Graphic arts, Neural networks, Classifiers, Combination, Regression, Estimators
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-3545DOI: 10.1007/s005210070016ISI: 000090150400009Scopus ID: 2-s2.0-0034344209OAI: oai:DiVA.org:hh-3545DiVA, id: diva2:285831
Available from: 2010-01-13 Created: 2009-12-01 Last updated: 2018-03-23Bibliographically approved

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Verikas, AntanasBergman, L.

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