A learning framework for morphological operators using counter-harmonic mean

Jonathan Masci, Jesús Angulo, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

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. © 2013 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages329-340
Number of pages12
DOIs
StatePublished - Sep 24 2013
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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