Abstract
Segmentation based on color, instead of intensity only, pro- vides an easier distinction between materials, on the condition that ro- bustness against irrelevant parameters is achieved, such as illumination source, shadows, geometry and camera sensitivities. Modeling the phys- ical 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.
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Geusebroek, JM., Dev, A., van den Boomgaard, R., Smeulders, A.W.M., Cornelissen, F., Geerts, H. (1999). Color Invariant Edge Detection. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_43
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DOI: https://doi.org/10.1007/3-540-48236-9_43
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