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

A.C. Jalba, M.H.F. Wilkinson, J.B.T.M. Roerdink

    Research output: Contribution to journalArticleAcademicpeer-review

    63 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)901-915
    JournalPattern Recognition
    Issue number5
    Publication statusPublished - 2004


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