Morphological hat-transform scale spaces and their use in pattern classification

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Abstract

In this paper we present a multi-scale method based on mathematical morphology which can successfully be used in pattern classification tasks. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built. The most important features are extracted from the scale spaces by unsupervised cluster analysis, and the resulting pattern vectors provide the input of a decision tree classifier. We report classification results obtained using contour features, texture features, and a combination of these. The method has been tested on two large sets, a database of diatom images and a set of images from the Brodatz texture database. For the diatom images, the method is applied twice, once on the curvature of the outline (contour), and once on the grey-scale image itself. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)901-915
Number of pages15
JournalPattern recognition
Volume37
Issue number5
DOIs
Publication statusPublished - May-2004

Keywords

  • mathematical morphology
  • scale space
  • top-hat transform
  • bottom-hat transform
  • connected operators
  • pattern classification
  • decision trees
  • diatom images
  • Brodatz textures
  • MATHEMATICAL MORPHOLOGY
  • CONNECTED OPERATORS
  • MOMENT INVARIANTS
  • PLANAR CURVES
  • RECOGNITION
  • FILTERS
  • MULTISCALE
  • OPENINGS
  • IMAGE

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