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International Symposium on Neural Networks

ISNN 2009: Advances in Neural Networks – ISNN 2009 pp 520–529Cite as

Morphological Hetero-Associative Memories Applied to Restore True-Color Patterns

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

Abstract

Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications by additions/subtractions and maximums/minimums. This type of associative model has been applied to different pattern recognition problems including face localization and reconstruction of gray scale images. Despite of his power, it has not been applied to problems involving true-color patterns. In this paper we describe how a Morphological Hetero-associative Memory (MHAM) can be applied in problems that involve true-color patterns. In addition, a study of the behavior of this associative model in the reconstruction of true-color images is performed using a benchmark of 14400 images altered by different type of noises.

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Authors and Affiliations

  1. Centro de Investigación en Computación – IPN, Av. Juan de Dios Batíz, esquina con Miguel Othón de Mendizábal, Ciudad de México, 07738, México

    Roberto A. Vázquez & Humberto Sossa

Authors
  1. Roberto A. Vázquez
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  2. Humberto Sossa
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Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Vázquez, R.A., Sossa, H. (2009). Morphological Hetero-Associative Memories Applied to Restore True-Color Patterns. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_57

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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