Abstract
An approach to analyzing the degrees of invariance of chromatic characteristics is proposed in this paper. In many vision applications, it is desirable that the chromatic characteristics of objects in images taken under different lighting conditions could remain constant. However, the invariance properties of chromatic characteristics are subject to the lighting conditions. In order to be able to apply to dynamic scenes, we consider three fundamental lighting sources: diffuse, ambient, and directed lightings. Any illumination condition can be approximated as a combination of the three lighting sources. The proposed degree of chromatic invariance is defined based on the chromatic characteristic behaviors under different illumination conditions. A lot of image samples under different illumination conditions are utilized, and from experimental results, we conclude that chromatic characteristics {H, C, C λ } are most stable and suitable for the vision applications.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Angelopoulou, E., Lee, S.W., Bajcsy, R.: Spectral Gradient: A Material Descriptor Invariant to Geometry and Incident Illumination. In: The 7th IEEE Int’l Conf. on Computer Vision, pp. 861–867 (1999)
Chang, S.L., Chen, L.S., Chung, Y.C., Chen, S.W.: Automatic license plate recognition. IEEE Trans. on Intelligent Transportation Systems 5(1), 42–54 (2004)
Chung, Y.C., Chang, S.L., Wang, J.M., Chen, S.W.: An Improved Intrinsic Images Extraction from a Single Image with Integrated Measures. In: IASTED International Conf. on Artificial Intelligence and Applications, Innsbruck, Austria, pp. 356–361 (2005)
Fang, C.Y., Chen, S.W., Fuh, C.S.: Automatic Change Detection of Driving Environments in a Vision-Based Driver Assistance System. IEEE Trans. on Neural Networks 14(3), 646–657 (2003)
Finlayson, G.D., Hordley, S.D.: Color Constancy at a Pixel. Journal of the Optical Society of America 18(2), 253–264 (2001)
Geusebroek, J.M., Gevers, T., Smeulders, A.W.M.: The Kubelka-Munk Theory for Color Image Invariant Properties. In: The 1st Conf. on Color in Graphics, Imaging, and Vision, pp. 463–467 (2002)
Geusebroek, J.M., van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(12), 1338–1350 (2001)
Healey, G., Jain, A.: Retrieving Multispectral Satellite Images Using Physics-Based Invariant Representations. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 842–848 (1996)
Kamijo, S., Matsushita, Y., Ikeuchi, K., Sakauchi, M.: Traffic Monitoring and Accident Detection at Intersections. IEEE Trans. on Intelligent Transportation Systems 1(2), 108–118 (2000)
Shafer, S.A.: Using Color to Separate Reflection Components. Color Resolution Applications 10(4), 210–218 (1985)
The Purdue RVL Specularity Image Database, http://rvl1.ecn.purdue.edu/RVL/specularity_database/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chung, YC., Chang, SL., Cherng, S., Chen, SW. (2006). The Invariance Properties of Chromatic Characteristics. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_13
Download citation
DOI: https://doi.org/10.1007/11949534_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
eBook Packages: Computer ScienceComputer Science (R0)