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Situation Awareness in Colour Printing and Beyond
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-8804-5884
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 [en]
Machine learning, Data mining, Colour printing, Smart homes
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-25318Libris ID: 16559681ISBN: 978-91-87045-12-7 ISBN: 978-91-87045-11-0 OAI: oai:DiVA.org:hh-25318DiVA: diva2:716365
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
List of papers
1. Advances in computational intelligence-based print quality assessment and control in offset colour printing
Open this publication in new window or tab >>Advances in computational intelligence-based print quality assessment and control in offset colour printing
2011 (English)In: Expert systems with applications, ISSN 0957-4174, Vol. 38, no 10, 13441-13447 p.Article in journal (Refereed) Published
Abstract [en]

Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2011
Keyword
Print quality, Decision support, Expert system, Image analysis, Quality inspection
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-15314 (URN)10.1016/j.eswa.2011.04.035 (DOI)000292169500156 ()2-s2.0-79957992693 (Scopus ID)
Available from: 2011-06-09 Created: 2011-06-09 Last updated: 2015-09-11Bibliographically approved
2. Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing
Open this publication in new window or tab >>Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing
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
Series
IEEE International Conference on Fuzzy Systems, ISSN 1098-7584 ; 2010
Keyword
halftone dot, image processing, hough transform
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-5601 (URN)10.1109/FUZZY.2010.5584433 (DOI)000287453602036 ()2-s2.0-78549265175 (Scopus ID)978-1-4244-6920-8 (ISBN)
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
3. Assessing, exploring, and monitoring quality of offset colour prints
Open this publication in new window or tab >>Assessing, exploring, and monitoring quality of offset colour prints
2013 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 46, no 4, 1427-1441 p.Article in journal (Refereed) Published
Abstract [en]

Variations in offset print quality relate to numerous parameters of printing press and paper. To maintain a constant high print quality press operators need to assess, explore and monitor quality of prints. Today assessment is mainly done manually. This paper presents a novel system for assessing and predicting values of print quality attributes, where the adopted, random forests (RFs)-based, modeling approach also allows quantifying the influence of different paper and press parameters on print quality. In contrast to other print quality assessment systems the proposed system utilises common, simple print marks known as double grey-bars. Novel virtual sensors assessing print quality attributes using images of double grey-bars are presented. The inferred influence of paper and printing press parameters on quality of colour prints shows clear relation with known print quality conditions. Thorough analysis and categorisation of related work is also given in the paper. (C) 2012 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2013
Keyword
Virtual sensor, Random forests, Print quality assessment, Decision support, Variable importance, Bar-code reader
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-20278 (URN)10.1016/j.measurement.2012.11.037 (DOI)000316431100009 ()2-s2.0-84873030720 (Scopus ID)
Projects
PPQ
Funder
Knowledge Foundation, 2007/0279
Available from: 2013-01-02 Created: 2013-01-02 Last updated: 2017-03-30Bibliographically approved
4. Assessing print quality by machine in offset colour printing
Open this publication in new window or tab >>Assessing print quality by machine in offset colour printing
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
Keyword
Random forest, Variable importance, t-Stochastic neighbour embedding, Print quality, Subjective quality assessment
National Category
Computer Science
Identifiers
urn:nbn:se:hh:diva-20277 (URN)10.1016/j.knosys.2012.07.022 (DOI)000313761800006 ()2-s2.0-84870066230 (Scopus ID)
Projects
PPQ
Funder
Knowledge Foundation, 2007/0279
Available from: 2013-01-02 Created: 2013-01-02 Last updated: 2017-03-30Bibliographically approved
5. Detecting and exploring deviating behaviour of people in their own homes
Open this publication in new window or tab >>Detecting and exploring deviating behaviour of people in their own homes
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25317 (URN)
Projects
SA3L - Situation Awareness for Ambient Assisted Living
Funder
Knowledge Foundation
Note

Som manuskript i avhandling. As manuscript in dissertation.

Available from: 2014-05-09 Created: 2014-05-09 Last updated: 2016-04-08Bibliographically approved

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