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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben
    University of Malta, Msida, Malta.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion2016In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), Piscataway: IEEE, 2016, article id 7791208Conference paper (Refereed)
    Abstract [en]

    Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.

  • 2.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bae, Juhee
    School of Informatics, University of Skövde, Sweden.
    Incremental causal discovery and visualization2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper (Refereed)
    Abstract [en]

    Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

  • 3.
    Nilsson, Carl Magnus
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Lennart
    Halmstad University, School of Information Technology.
    Carlsson, Jörgen
    Halmstad University, School of Information Technology.
    Kristiansson, Per
    Study of pigment and optical response distributions in newsprint1998In: 1998 proceedings - Technical Association of the Graphic Arts, TAGA, Rochester, NY, 1998, p. 737-748Conference paper (Refereed)
    Abstract [en]

    The quality of the printed screen dots is one of the critical factors that determine the over all quality of a printed halftone image. In this paper the spatial distribution of pigment is recorded and its relation to optical response and local paper grammage in printed screen dots on newsprint is analyzed. It is shown that light scattering inside the paper structure to some extent hide large irregularities in the pigment distribution. It is also shown that a hole in a screen dot will be optical mitigate by the paper structure as a result of multiple light scattering inside the bulk. We also show experimentally the hue shift in the border zone of the screen dot. A halo of light around the pigment area, which shifts from cyan towards green, has been determined around a cyan screen dot. Influence from the cyan pigment can be spotted in the optical response up to a distance of at least 20 ÎŒm from the measured pigment particle.

  • 4.
    Nugent, Christopher
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. University of Ulster, Jordanstown, North Ireland.
    Synnott, Jonathan
    University of Ulster, Jordanstown, North Ireland.
    Gabrielli, Celeste
    Marche Polytechnic University, Ancona, Italy.
    Zhang, Shuai
    University of Ulster, Jordanstown, North Ireland.
    Espinilla, Macarena
    University of Jaén, Jaen, Spain..
    Calzada, Alberto
    University of Ulster, Jordanstown, North Ireland.
    Lundström, Jens
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Cleland, Ian
    University of Ulster, Jordanstown, North Ireland.
    Synnes, Kare
    Luleå university of Technology, Luleå, Sweden.
    Hallberg, Josef
    Luleå university of Technology, Luleå, Sweden.
    Spinsante, Susanna
    Marche Polytechnic University, Ancona, Italy.
    Ortiz Barrios, Miguel Angel
    Universidad de la Costa CUC, Barranquilla, Colombia.
    Improving the Quality of User Generated Data Sets for Activity Recognition2016In: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II / [ed] Garcia, CR CaballeroGil, P Burmester, M QuesadaArencibia, A, Amsterdam: Springer Publishing Company, 2016, p. 104-110Conference paper (Refereed)
    Abstract [en]

    It is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1-2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.

  • 5.
    Pink, Sarah
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). RMIT University, Melbourne, Australia.
    Ruckenstein, Minna
    University of Helsinki, Helsinki, Finland.
    Willim, Robert
    Lund University, Lund, Sweden.
    Duque, Melisa
    RMIT University, Melbourne, Australia.
    Broken data: Conceptualising data in an emerging world2018In: Big Data and Society, ISSN 2053-9517, E-ISSN 2053-9517, Vol. 5, no 1Article in journal (Refereed)
    Abstract [en]

    In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of personal self-tracking data; the data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds.

  • 6.
    Sequeira, Ana F.
    et al.
    University of Reading, Reading, United Kingdom.
    Chen, Lulu
    University of Reading, Reading, United Kingdom.
    Wild, Peter
    AIT Austrian Institute of Technology, Vienna, Austria.
    Ferryman, James
    University of Reading, Reading, United Kingdom.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Raja, Kiran B.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Raghavendra, R.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Busch, Christoph
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Cross-Eyed: Cross-Spectral Iris/Periocular Recognition Database and Competition2016In: Proceedings of the 15th International Conference of the Biometrics Special Interest Group / [ed] Arslan Brömme, Christoph Busch, Christian Rathgeb & Andreas Uhl, Piscataway, N.J.: IEEE, 2016Conference paper (Refereed)
    Abstract [en]

    This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross- spectrum iris and periocular recognition. Six submissions were evaluated for cross-spectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.

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