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Assessing print quality by machine in offset colour printing
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0001-8804-5884
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
2013 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 37, 70-79 p.Article in journal (Refereed) Published
Abstract [en]

Information processing steps in printing industry are highly automated, except the last one print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several printquality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer. (C) 2012 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2013. Vol. 37, 70-79 p.
Keyword [en]
Random forest, Variable importance, t-Stochastic neighbour embedding, Print quality, Subjective quality assessment
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-20277DOI: 10.1016/j.knosys.2012.07.022ISI: 000313761800006Scopus ID: 2-s2.0-84870066230OAI: oai:DiVA.org:hh-20277DiVA: diva2:581723
Projects
PPQ
Funder
Knowledge Foundation, 2007/0279
Available from: 2013-01-02 Created: 2013-01-02 Last updated: 2017-03-30Bibliographically approved
In thesis
1. Situation Awareness in Colour Printing and Beyond
Open this publication in new window or tab >>Situation Awareness in Colour Printing and Beyond
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014. 51 p.
Series
Halmstad University Dissertations, 6
Keyword
Machine learning, Data mining, Colour printing, Smart homes
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25318 (URN)978-91-87045-12-7 (ISBN)978-91-87045-11-0 (ISBN)
Public defence
2014-06-13, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 13:15 (English)
Opponent
Supervisors
Projects
PPQSA3L
Funder
Knowledge Foundation
Available from: 2014-05-09 Created: 2014-05-09 Last updated: 2015-09-11Bibliographically approved

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Citation style
  • apa
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