Segmentation based on color, instead of intensity only, pro-vides an easier distinction between materials, on the condition that robustness against irrelevant parameters is achieved, such as illumination source, shadows, geometry and camera sensitivities. Modeling the physical process of the image formation provides insight into the effect of different parameters on object color. In this paper, a color differential geometry approach is used to detect material edges, invariant with respect to illumination color and imaging conditions. The performance of the color invariants is demonstrated by some real-world examples, showing the invariants to be successful in discounting shadow edges and illumination color.
CITATION STYLE
Geusebroek, J. M., Dev, A., van den Boomgaard, R., Smeulders, A. W. M., Cornelissen, F., & Geerts, H. (1999). Color invariant edge detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1682, pp. 459–464). Springer Verlag. https://doi.org/10.1007/3-540-48236-9_43
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