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Proceedings Paper

Integrating colour models for more robust feature detection
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Paper Abstract

The choice of a colour space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. Since there are many colour spaces available, the problem is how to automatically select the weighting to integrate the colour spaces in order to produce the best result for a particular task. In this paper we propose a method to learn these weights, while exploiting the non-perfect correlation between colour spaces of features through the principle of diversification. As a result an optimal trade-off is achieved between repeatability and distinctiveness. The resulting weighting scheme will ensure maximal feature discrimination. The method is experimentally verified for three feature detection tasks: Skin colour detection, edge detection and corner detection. In all three tasks the method achieved an optimal trade-off between (colour) invariance (repeatability) and discriminative power (distinctiveness).

Paper Details

Date Published: 16 January 2006
PDF: 9 pages
Proc. SPIE 6061, Internet Imaging VII, 60610D (16 January 2006); doi: 10.1117/12.650619
Show Author Affiliations
F. Aldershoff, Univ. of Amsterdam (Netherlands)
Th. Gevers, Univ. of Amsterdam (Netherlands)
H. Stokman, Univ. of Amsterdam (Netherlands)


Published in SPIE Proceedings Vol. 6061:
Internet Imaging VII
Simone Santini; Raimondo Schettini; Theo Gevers, Editor(s)

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