Measurement of Color Invariants

This paper presents the measurement of object reflectance from color images. We exploit the Gaussian scalespace paradigm to define a 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, summarized by: shadow highlights illumination intensity illumination color H + + + – C + – + – W – – + – E – – – – 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. This work is sponsored by Janssen Research Foundation, Beerse, Belgium.