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Proceedings Paper

Dimensionless color features
Author(s): Albrecht Melan; Stephan Rudolph
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Paper Abstract

Feature extraction is a major processing step in pattern recognition. To classify similar objects into the correct object class the selected image features should represent the desired objects invariance. This means any two objects, which are similar according to the given similarity postulate, should have identical features so that the classificator maps them to the same object class. If the similarity postulate requires invariance under translation, scaling, and rotation, then geometric moments have been shown to exhibit appropriate properties. As an extension to the traditional use of geometric moments it is possible to assign physical dimensions to geometric moments. By this means the application of dimensional analysis becomes possible. For the case of color images the spectral power distribution can be used directly to derive dimensionless features for color objects. The construction of these dimensionless color features and their properties for color object classification will be discussed.

Paper Details

Date Published: 31 July 2002
PDF: 10 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477623
Show Author Affiliations
Albrecht Melan, Univ. of Stuttgart (Germany)
Stephan Rudolph, Univ. of Stuttgart (Germany)

Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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