_:n299b451a1b6c48cf87e27db12d75df1fxb40
<http://schema.org/description>
"Microfading Spectrometry (MFS) is a method for assessing light sensitivity\ncolor (spectral) variations of cultural heritage objects. Each measured point\non the surface gives rise to a time-series of stochastic observations that\nrepresents color fading over time. Color degradation is expected to be\nnon-decreasing as a function of time and stabilize eventually. These properties\ncan be expressed in terms of the derivatives of the functions. In this work, we\npropose spatially correlated splines-based time-varying functions and their\nderivatives for modeling and predicting MFS data collected on the surface of\nrock art paintings. The correlation among the splines models is modeled using\nGaussian process priors over the spline coefficients across time-series. A\nmultivariate covariance function in a Gaussian process allows the use of\ntrichromatic image color variables jointly with spatial locations as inputs to\nevaluate the correlation among time-series, and demonstrated the colorimetric\nvariables as useful for predicting new color fading time-series. Furthermore,\nmodeling the derivative of the model and its sign demonstrated to be beneficial\nin terms of both predictive performance and application-specific\ninterpretability."
<https://api.deepai.org/profile/virgilio-gomez-rubio>