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Hidden Markov Models For Two-Dimensional Data

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Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 226))

Abstract

Hidden Markov models are well-known methods for image processing. They are used in many areas where 1D data are processed. In the case of 2D data, there appear some problems with application HMM. There are some solutions, but they convert input observation from 2D to 1D, or create parallel pseudo 2D HMM, which is set of 1D HMMs in fact. This paper describes authentic 2D HMM with two-dimensional input data, and its application for pattern recognition in image processing.

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Authors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Dabrowskiego Street 73, 42-200, Czestochowa, Poland

    Janusz Bobulski

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  1. Janusz Bobulski
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Correspondence to Janusz Bobulski .

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Editors and Affiliations

  1. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Robert Burduk

  2. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Konrad Jackowski

  3. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Marek Kurzynski

  4. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Michał Wozniak

  5. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St, Wroclaw, 50-370, Poland

    Andrzej Zolnierek

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© 2013 Springer International Publishing Switzerland

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Bobulski, J. (2013). Hidden Markov Models For Two-Dimensional Data. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-00969-8_14

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00968-1

  • Online ISBN: 978-3-319-00969-8

  • eBook Packages: EngineeringEngineering (R0)

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