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International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

IPMU 2016: Information Processing and Management of Uncertainty in Knowledge-Based Systems pp 599–611Cite as

Similarity Measures for Radial Data

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 610))

Abstract

Template-based methods for image processing hold a list of advantages over other families of methods, e.g. simplicity and ability to mimic human behaviour. However, they also demand a careful design of the pattern representatives as well as that of the operators in charge of measuring/detecting their presence in the data. This work presents a method for fingerprint analysis, specifically for singular point detection, based on template matching. The matching process sparks the need for similarity measures able to cope with radial data. As a result, we introduce the concepts of Restricted Radial Equivalence Function (RREF) and Radial Similarity Measure (RSM), further used to evaluate the perceptual closeness of scalar and vectorial pieces of radial data, respectively. Our method, which goes by the name of Template-based Singular Point Detection method (TSPD), has qualitative advantages over other alternatives, and proves to be competitive with state-of-the art methods in quantitative terms.

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Notes

  1. 1.

    The method in [17] is indeed used as baseline contender for the experiments in Sect. 5.

  2. 2.

    The labels for this experiment can be downloaded from [4].

  3. 3.

    The threshold for each method, including Liu’s and Poincaré, has been manually set to optimize the results.

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Acknowledgements

The authors gratefully acknowledge the financial support of the Spanish Ministry of Science (project TIN2013-40765-P), as well as the financial support of the Research Foundation Flanders (FWO project 3G.0838.12.N).

Author information

Authors and Affiliations

  1. Dpto. Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    Carlos Lopez-Molina, Cedric Marco-Detchart, Javier Fernandez, Juan Cerron, Mikel Galar & Humberto Bustince

  2. Department of Mathematical Modeling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium

    Carlos Lopez-Molina

  3. Institute of Smart Cities, Universidad Publica de Navarra, Pamplona, Spain

    Humberto Bustince

Authors
  1. Carlos Lopez-Molina
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  2. Cedric Marco-Detchart
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  3. Javier Fernandez
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  4. Juan Cerron
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  5. Mikel Galar
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  6. Humberto Bustince
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Corresponding author

Correspondence to Carlos Lopez-Molina .

Editor information

Editors and Affiliations

  1. INESC-ID,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Joao Paulo Carvalho

  2. LIP 6, Université Pierre et Marie Curie, Paris, France

    Marie-Jeanne Lesot

  3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  4. IDMEC,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Susana Vieira

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

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© 2016 Springer International Publishing Switzerland

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Lopez-Molina, C., Marco-Detchart, C., Fernandez, J., Cerron, J., Galar, M., Bustince, H. (2016). Similarity Measures for Radial Data. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-40596-4_50

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  • DOI: https://doi.org/10.1007/978-3-319-40596-4_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40595-7

  • Online ISBN: 978-3-319-40596-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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