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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Evaluation of Colour Models for Computer Vision Using Cluster Validation Techniques
verfasst von : David Budden, Shannon Fenn, Alexandre Mendes, Stephan Chalup
Erschienen in: RoboCup 2012: Robot Soccer World Cup XVI
Verlag: Springer Berlin Heidelberg
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Computer vision systems frequently employ colour segmentation as a step of feature extraction. This is particularly crucial in an environment where important features are colour-coded, such as robot soccer. This paper describes a method for determining an appropriate colour model by measuring the compactness and separation of clusters produced by the
k
-means algorithm.
RGB
,
HSV
,
YC
b
C
r
and
CIE L*a*b*
colour models are assessed for a selection of artificial and real images, utilising an implementation of the Dunn’s-based cluster validation index. The effectiveness of the method is assessed by qualitatively comparing the relative correctness of the segmentation to the results of the cluster validation. Results demonstrate a significant variation in segmentation quality among colour spaces, and that YC
b
C
r
is the best choice for the DARwIn-OP platform tested.