Robust Specularity Detection from a Single Multi-illuminant Color Image

Abstract

How can one identify specularities when an object is illuminated by light that varies in spectrum with direction from the surface? A linear model of color shading can answer this question and also recover surface orientation in non-specular regions using only a single color image of the surface taken under a set of illuminants whose positions, strengths, and spectral content need not be known a priori. The shape-from-color method is based on a Lambertian model. For such a reflectance model the surface normal is related in a linear way to the measured RGB color. Linearity means that the Gaussian sphere is transformed into an ellipsoid in color space, and one can solve for the ellipsoid using least squares; surface normals are recovered only up to an overall orthogonal transformation unless additional constraints are employed. When specularities are present, the least-squares method no longer works. If, however, one views specularities as outliers to the underlying color ellipsoid, then a robust method can still find that surface in RGB space. Here a least-median-of-squares method is used to recover shape and detect specularities at the same time.