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Progress in Artificial Intelligence

, Volume 6, Issue 2, pp 121–132 | Cite as

Baddeley’s Delta metric for local contrast computation in hyperspectral imagery

  • C. Lopez-Molina
  • D. Ayala-Martini
  • A. Lopez-Maestresalas
  • H. Bustince
Regular Paper

Abstract

Recent years have brought a quick decay in prize of hyperspectral imagery equipment. As a consequence, new applications have appeared, a relevant example being the analysis of agro-food materials. Such applications need to be grounded on dedicated image processing operators, which fully accomplish with (and exploit) the characteristics of hyperspectral imagery. In this regard, we study the quantitative comparison of spectra, which can be further used to produce a variety of image processing operators. Specifically, we propose the use of Baddeley’s Delta metric for the comparison of spectra. Our method has theoretical advantages over classical bandwise comparison measures, which are often inconsistent with human perception of dissimilarity between spectra. Our proposal is put to the test in the context of local contrast computation, with application to item segmentation of in-laboratory imagery.

Keywords

Hyperspectral imagery Comparison measures Baddeley’s Delta metric Local contrast Image segmentation 

Notes

Acknowledgements

This research was supported by the National Institute for Agricultural and Food Research and Technology (INIA) through the Project RTA2013-00006-C03-03 and also by the Spanish Ministry of Science (Project TIN-2016-77356-P).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • C. Lopez-Molina
    • 1
  • D. Ayala-Martini
    • 2
  • A. Lopez-Maestresalas
    • 2
  • H. Bustince
    • 1
  1. 1.Dpto. Automatica y ComputacionUniversidad Publica de NavarraPamplonaSpain
  2. 2.Dpto. Proyectos e Ing. RuralUniversidad Publica de NavarraPamplonaSpain

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