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A Learning Framework for Morphological Operators Using Counter–Harmonic Mean

  • Jonathan Masci
  • Jesús Angulo
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7883)

Abstract

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.

Keywords

mathematical morphology convolutional networks online learning machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jonathan Masci
    • 1
  • Jesús Angulo
    • 2
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIA – USI – SUPSIManno–LuganoSwitzerland
  2. 2.CMM-Centre de Morphologie Mathématique, Mathématiques et SystèmesMINES ParisTechFrance

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