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Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). (PPQ)ORCID iD: 0000-0001-8804-5884
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). (PPQ)ORCID iD: 0000-0003-2185-8973
2010 (English)In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), Piscataway, NJ: IEEE Press, 2010, 1145-1151 p.Conference paper, (Refereed)
Abstract [en]

Nowadays in printing industry most of information processing steps are highly automated, except the last one–print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2010. 1145-1151 p.
Series
IEEE International Conference on Fuzzy Systems, ISSN 1098-7584 ; 2010
Keyword [en]
halftone dot, image processing, hough transform
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-5601DOI: 10.1109/FUZZY.2010.5584433ISI: 000287453602036Scopus ID: 2-s2.0-78549265175ISBN: 978-1-4244-6920-8 OAI: oai:DiVA.org:hh-5601DiVA: diva2:350120
Conference
WCCI 2010 IEEE World Congress on Computational Intelligence, FUZZ-IEEE
Projects
PPQ
Note

©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2010-09-10 Created: 2010-09-07 Last updated: 2015-09-11Bibliographically 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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