| Summary: | In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and <inline-formula> <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>E</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics> </math> </inline-formula> color differences. Values of less than 3 CIELAB units were achieved for <inline-formula> <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>E</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics> </math> </inline-formula>. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and <inline-formula> <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msubsup> <mi>E</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics> </math> </inline-formula>. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.
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