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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References
Cumani, A.: Edge detection in multispectral images. CVGIP: Graphical Models and Image Processing 53 (1991) 40–51
Dev, A., van den Boomgaard, R.: Color and scale: The spatial structure of color images, Technical report, ISIS institute, Department of Computer Science, University of Amsterdam, Amsterdam, The Netherlands (1999)
Florack, L.M.J., Romeny, B.M.tH., Koenderink, J.J., Viergever, M.A.: Cartesian differential invariants in scale-space. Journal of Mathematical Imaging and Vision 3 (1993) 327–348
Gershon, R., Jepson, D., Tsotsos, J.K.: Ambient illumination and the determination of material changes. J. Opt. Soc. Am. A 3 (1986) 1700–1707
Gevers, T., Smeulders, A.W.M.: Color based object recognition. Pat. Rec. 32 (1999) 453–464
Hering, E.: Outlines of a Theory of the Light Sense. Harvard University Press, Cambridge, MS (1964)
Judd, D.B., Wyszecki, G.: Color in Business, Science, and Industry. Wiley, New York, NY (1975)
Koenderink, J.J., van Doorn, A.J.: Receptive field families. Biol. Cybern. 63 (1990) 291–297
Koenderink, J.J., Kappers, A.: Color Space. Utrecht University, The Netherlands (1998)
Mielenz, K.D., Eckerle, K.L., Madden, R.P., Reader, J.: New reference spectrophotometer. Appl. Optics 12 (1973) 1630–1641
Olver, P., Sapiro, G., Tannenbaum, A.: Differential invariant signatures and flows in computer vision: A symmetry group approach. In: Geometry-Driven Diffusion in Computer Vision, ter Haar Romeny BM (ed). Kluwer Academic Publishers, Boston (1994) 255–306
Shafer, S.A.: Using color to separate reflection components. Color Res. Appl. 10 (1985) 210–218
Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley, New York, NY (1982)
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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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