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Improving the Discrimination Capability with an Adaptive Synthetic Discriminant Function Filter

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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

In this paper a new adaptive correlation filter based on synthetic discriminant functions (SDF) for reliable pattern recognition is proposed. The information about an object to be recognized and false objects as well as background to be rejected is used in an iterative procedure to design the adaptive correlation filter with a given discrimination capability. Computer simulation results obtained with the proposed filter in test scenes are compared with those of various correlation filters in terms of discrimination capability.

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Authors and Affiliations

  1. Department of Computer Sciences, Division of Applied Physics, CICESE, Ensenada, B.C, 22860, Mexico

    J. Ángel González-Fraga, Víctor H. Díaz-Ramírez & Vitaly Kober

  2. Optics Department, Division of Applied Physics, CICESE, Ensenada, B.C, 22860, Mexico

    Josué Álvarez-Borrego

Authors
  1. J. Ángel González-Fraga
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  2. Víctor H. Díaz-Ramírez
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  3. Vitaly Kober
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  4. Josué Álvarez-Borrego
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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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González-Fraga, J.Á., Díaz-Ramírez, V.H., Kober, V., Álvarez-Borrego, J. (2005). Improving the Discrimination Capability with an Adaptive Synthetic Discriminant Function Filter. 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_11

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  • DOI: https://doi.org/10.1007/11492542_11

  • 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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