Morphological hat-transform scale spaces and their use in pattern classification
A.C. Jalba, M.H.F. Wilkinson and J.B.T.M. Roerdink. Morphological hat-transform scale spaces and their use in pattern classification. Pattern Recognition , 37(5), pp. 901-915, 2004.
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.
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