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Image Quality Enhancement for Liquid Bridge Parameter Estimation with DTCNN

  • Miguel A. Jaramillo
  • J. álvaro Fernández
  • José M. Montanero
  • Fernando Zayas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

This work present the use of a neural structure to augment the quality of noisy images of liquid bridges to obtain a clear representation of its border in order to determine the acceleration that it is suffering. The used network is a three layers Discrete Time Cellular Neural Network in which the last one performs the contour highlighting through the adaptive definition of the gain and threshold of their output functions. Then an easy algorithm extracts a curve from the border.

Keywords

Output Function Liquid Bridge Bond Number Cellular Neural Network Axial Acceleration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Miguel A. Jaramillo
    • 1
  • J. álvaro Fernández
    • 1
  • José M. Montanero
    • 1
  • Fernando Zayas
    • 1
  1. 1.Dpto. Electrónica e Ingeniería Electromecánica Escuela de Ingenierías IndustrialesUniversidad de ExtremaduraBadajozSPAIN

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