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Multispectral Image Segmentation by Energy Minimization for Fruit Quality Estimation

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Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

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Abstract

This article presents the results of an unsupervised segmentation algorithm in multispectral images. The algorithm uses a minimization function which takes into account each band intensity information together with global edge criterion. Due to the unsupervised nature of the procedure, it can adapt itself to the huge variability of intensities and shapes of the image regions. Results shows the effectiveness of the method in multispectral fruit inspection applications and in remote sensing tasks.

This work has been partly supported by grants DPI2001-2956-C02-02 from Spanish CICYT and IST-2001-37306 from the European Union

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Author information

Authors and Affiliations

  1. Dept. Lenguajes y Sistemas Informáticos, Jaume I Univerisity,  

    Adolfo Martínez-Usó, Filiberto Pla & Pedro García-Sevilla

Authors
  1. Adolfo Martínez-Usó
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  2. Filiberto Pla
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  3. Pedro García-Sevilla
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica,, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. Instituto Superior Técnico, CERENA-Centro de Recursos Naturais e Ambiente, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Martínez-Usó, A., Pla, F., García-Sevilla, P. (2005). Multispectral Image Segmentation by Energy Minimization for Fruit Quality Estimation. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_84

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  • DOI: https://doi.org/10.1007/11492542_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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