In this paper we propose an approach to color classification and image segmentation in non-stationary environments. Our goal is to cope with changing illumination condition by on-line adapting both the parametric color model and its structure/complexity. Other authors used parametric statistics to model color distribution in segmentation and tracking problems, but with a fixed complexity model. Our approach is able to on-line adapt also the complexity of the model, to cope with large variations in the scene illumination and color temperature. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Anzani, F., Bosisio, D., Matteucci, M., & Sorrenti, D. G. (2006). On-line color calibration in non-stationary environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4020 LNAI, pp. 396–407). Springer Verlag. https://doi.org/10.1007/11780519_35
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