19 March 2008 Recovering layers of brush strokes through statistical analysis of color and shape: an application to van Gogh's Self portrait with grey felt hat
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Abstract
We used digital image processing and statistical clustering algorithms to segment and classify brush strokes in master paintings based on two-dimensional space and three-dimensional chromaticity coordinates. For works executed in sparse overlapping brush strokes our algorithm identifies candidate clusters of brush strokes of the top (most visible) layer and digitally removes them. Then, it applies modified inpainting algorithms based on statistical structure of strokes to fill in or "inpaint" the remaining, partially hidden brush strokes. This processes can be iterated, to reveal and fill in successively deeper (partially hidden) layers of brush strokes-a process we call "de-picting." Of course, the reconstruction of strokes at each successively deeper layer is based on less and less image data from the painting and requires cascading estimates and inpainting; as such our methods yield poorer accuracy and fidelity for such deeper layers. Our current software is semi-automatic; the operator such as a curator or art historian guides certain steps. Future versions of our software will be fully automatic, and estimate more accurate statistical models of the brush strokes in the target painting yield better estimates of hidden brush strokes. Our software tools may aid art scholars in characterizing the images of paintings as well as the working methods of some master painters.
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Morteza Shahram, David G. Stork, David Donoho, "Recovering layers of brush strokes through statistical analysis of color and shape: an application to van Gogh's Self portrait with grey felt hat", Proc. SPIE 6810, Computer Image Analysis in the Study of Art, 68100D (19 March 2008); doi: 10.1117/12.765773; https://doi.org/10.1117/12.765773
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