Measurement of color invariants

This paper presents the measurement of object reflectance from color images. We exploit the Gaussian scale-space paradigm to define framework for the robust measurement of object reflectance from color images. Illumination and geometrical invariant properties are derived from a physical reflectance model based on the Kubelka-Munk theory. Imaging conditions are assumed to be white illumination and matte, dull object or general object, respectively. Invariance is denoted by +, whereas sensitivity to the imaging condition is indicated by -. Invariance, discriminative power and localization accuracy of the color invariants is extensively investigated, showing the invariants to be successful in discounting shadow, illumination intensity, highlights, and noise. Experiments show the different invariants to be highly discriminative while maintaining invariance properties. The presented framework for color measurement is well-founded in physics as well as measurement science. The framework is thoroughly evaluated experimentally. Hence is considered more adequate than existing methods for the measurement of invariant color features.

[1]  Arnold W. M. Smeulders,et al.  Color Invariant Edge Detection , 1999, Scale-Space.

[2]  Arnold W. M. Smeulders,et al.  Color Based Object Recognition , 1997, ICIAP.

[3]  Guillermo Sapiro,et al.  Differential Invariant Signatures and Flows in Computer Vision: A Symmetry Group Approach , 1994, Geometry-Driven Diffusion in Computer Vision.

[4]  Ewald Hering.  Outlines of a theory of the light sense , 1964 .

[5]  Joachim Weickert,et al.  Scale-Space Theories in Computer Vision , 1999, Lecture Notes in Computer Science.

[6]  G. Wyszecki,et al.  Color Science Concepts and Methods , 1982 .

[7]  J. Cohen,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulas , 1968 .

[8]  R. Bajcsy,et al.  Color image segmentation with detection of highlights and local illumination induced by inter-reflections , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[9]  P. Kubelka,et al.  New Contributions to the Optics of Intensely Light-Scattering Materials. Part I , 1948 .

[10]  Bart M. ter Haar Romeny,et al.  Geometry-Driven Diffusion in Computer Vision , 1994, Computational Imaging and Vision.

[11]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[12]  Th. Gevers,et al.  Reflectance Based Edge Classification , 1999 .

[13]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Arnold W. M. Smeulders,et al.  Color-based object recognition , 1997, Pattern Recognit..

[15]  Arnold W. M. Smeulders,et al.  Color and Scale: The Spatial Structure of Color Images , 2000, ECCV.

[16]  John K. Tsotsos,et al.  Ambient illumination and the determination of material changes. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[17]  K NayarShree,et al.  Reflectance and texture of real-world surfaces , 1999 .

[18]  P. Kubelka,et al.  Errata: New Contributions to the Optics of Intensely Light-Scattering Materials. Part I , 1948 .

[19]  C LAPICQUE,et al.  Categorical in Aperture Color-Name and Surface Regions of A Color Modes Color Space , 2007 .

[20]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[21]  Audrey Tarrant,et al.  Color in Business, Science and Industry , 1976 .