Dimensionless color features

Albrecht Melan; Stephan Rudolph

doi:10.1117/12.477623

31 July 2002 Dimensionless color features

Albrecht Melan, Stephan Rudolph

Proceedings Volume 4729, Signal Processing, Sensor Fusion, and Target Recognition XI; (2002) https://doi.org/10.1117/12.477623
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Albrecht Melan and Stephan Rudolph "Dimensionless color features", Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); https://doi.org/10.1117/12.477623

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