A painting needs illumination to be visible. If the illumination is provided by an LCD data projector, different regions of the painting can be illuminated separately. Modern projectors have large color gamuts and can provide a wide range of illumination effects. One possible effect is to project a captured digital image of the painting onto the painting; the resulting superposition of like colors intensifies the contrast and saturation of the image. The opposite effect is to project the complement of the image onto the painting to "neutralize" it. When carefully done, with correct registration, the painting fades into a nearly uniform gray. Although a simple idea, in practice it is not trivial to accurately find the complementary color for each part of the painting, even when it is captured by a calibrated digital camera. This research examines the problems of accurately capturing the image, combining the projector gamut with typical paint reflectances, and determining the available range of complementary projector colors and the final lightness of the neutral image. The work was initially inspired by a student's fine art project, wherein computer animation was superimposed on a painting, bringing it to life.
To paraphrase Abraham Maslow: If the only tool you have is a hammer, every problem looks like a nail. We have a B-spline fitter customized for 3D color data, and many problems in color management can be solved with this tool. Whereas color devices were once modeled with extensive measurement, look-up tables and trilinear interpolation, recent improvements in hardware have made B-spline models an affordable alternative. Such device characterizations require fewer color measurements than piecewise linear models, and have uses beyond simple interpolation. A B-spline fitter, for example, can act as a filter to remove noise from measurements, leaving a model with guaranteed smoothness. Inversion of the device model can then be carried out consistently and efficiently, as the spline model is well behaved and its derivatives easily computed. Spline-based algorithms also exist for gamut mapping, the composition of maps, and the extrapolation of a gamut. Trilinear interpolation---a degree-one spline---can still be used after nonlinear spline smoothing for high-speed evaluation with robust convergence. Using data from several color devices, this paper examines the use of B-splines as a generic tool for modeling devices and mapping one gamut to another, and concludes with applications to high-dimensional and spectral data.