DocumentCode :
3082704
Title :
Advances in daylight statistical colour modelling
Author :
Alexander, Daniel
Author_Institution :
GRASP Lab., Pennsylvania Univ., Philadelphia, PA, USA
Volume :
1
fYear :
1999
fDate :
1999
Abstract :
In this paper, parametric statistical modelling of distributions of colour camera data is discussed. A review is provided with some analysis of the properties of some common models, which are generally based on an assumption of independence of the chromaticity and intensity components of colour data. Results of an empirical comparison of the performance of various models are also reviewed. These results indicate that such models are not appropriate for situations other than highly controlled environments. In particular, they perform poorly for daylight imagery. Here, a modification to existing statistical colour models is proposed and the resultant new models are assessed using the same methodology as for the previous results. This simple modification, which is based on the inclusion of an ambient term in the underlying physical model, is shown to have a major impact on the performance of the models in less constrained daylight environments
Keywords :
computer vision; image colour analysis; chromaticity; colour camera data; daylight statistical colour modelling; intensity components; parametric statistical modelling; performance; physical model; Cameras; H infinity control; Histograms; Image color analysis; Image segmentation; Independent component analysis; Land vehicles; Layout; Parametric statistics; Roads;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Conference_Location :
Fort Collins, CO
ISSN :
1063-6919
Print_ISBN :
0-7695-0149-4
Type :
conf
DOI :
10.1109/CVPR.1999.786957
Filename :
786957
Link To Document :
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