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New measures of homogeneity for image processing: an application to fingerprint segmentation

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Abstract

In this work, we axiomatically describe the concepts of total homogeneity and local homogeneity of a fuzzy set. We also present different construction methods by means of restricted equivalence functions, restricted dissimilarity functions and implication operators. We show that the most common homogeneity expressions can be obtained as particular cases of the constructions developed here. The usefulness of these functions is shown in segmentation of fingerprint images.

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Authors and Affiliations

  1. Departamento de Automática y Computación, Universidad Publica de Navarra, Campus Arrosadia s/n, 31006 , Pamplona, Spain

    A. Jurio, H. Bustince & M. Pagola

  2. Centre for Research and Technology of Agro-Environment and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 , Vila Real, Portugal

    P. Couto

  3. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4 , Edmonton, AB, Canada

    W. Pedrycz

Authors
  1. A. Jurio
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  2. H. Bustince
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  3. M. Pagola
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  4. P. Couto
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  5. W. Pedrycz
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Corresponding author

Correspondence to A. Jurio.

Additional information

Communicated by T.-P. Hong.

This paper has been partially supported by the Research Services of the Universidad Publica de Navarra and by the National Science Foundation of Spain, references TIN2010-15055 and TIN2011-29520.

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Jurio, A., Bustince, H., Pagola, M. et al. New measures of homogeneity for image processing: an application to fingerprint segmentation. Soft Comput 18, 1055–1066 (2014). https://doi.org/10.1007/s00500-013-1126-3

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