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Morphological hat-transform scale spaces and their use in pattern classification
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DOI：10.1016/j.patcog.2003.09.009.png)
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摘要
En 中文
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.
Keyword:
mathematical morphology
scale space
top-hat transform
bottom-hat transform
connected operators
pattern classification
decision trees
diatom images
Brodatz textures
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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