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International Workshop on Applications of Discrete Geometry and Mathematical Morphology

WADGMM 2010: Applications of Discrete Geometry and Mathematical Morphology pp 109–124Cite as

Machine Learning as a Preprocessing Phase in Discrete Tomography

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7346))

Abstract

In this paper we investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or – with the aid of the knowledge acquired – to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers.

This research was supported by the TÁMOP-4.2.2/08/1/2008-0008 program of the Hungarian National Development Agency, the European Union and the European Regional Development Fund. The work of M. Gara was also is supported by the European Union and co-funded by the European Social Fund under the project number TÁMOP-4.2.2/B-10/1-2010-0012. The work of P. Balázs was also supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and by the Hungarian Scientific Research Fund OTKA PD100950.

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Author information

Authors and Affiliations

  1. Department of Image Processing and Computer Graphics, University of Szeged, Árpád tér 2., H-6720, Szeged, Hungary

    Mihály Gara, Tamás Sámuel Tasi & Péter Balázs

Authors
  1. Mihály Gara
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  2. Tamás Sámuel Tasi
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  3. Péter Balázs
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Editor information

Editors and Affiliations

  1. HCI, University of Heidelberg, Germany, Speyerer Strasse 6, D-69115, Heidelberg, Germany

    Ullrich Köthe

  2. GIPSA-lab, 961 rue de la Houille Blanche, 38402, Saint Martin d’Hères cedex, France

    Annick Montanvert

  3. EC JRC Space Application Institute, T.P. 262, 21020, Ispra, Italy

    Pierre Soille

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Gara, M., Tasi, T.S., Balázs, P. (2012). Machine Learning as a Preprocessing Phase in Discrete Tomography. In: Köthe, U., Montanvert, A., Soille, P. (eds) Applications of Discrete Geometry and Mathematical Morphology. WADGMM 2010. Lecture Notes in Computer Science, vol 7346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32313-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-32313-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32312-6

  • Online ISBN: 978-3-642-32313-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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