In examining paintings, art historians use a wide variety of physico-chemical methods to determine, for example, the paints,
the ground (canvas primer) and any underdrawing the artist used. However, the art world has been little touched by signal
processing algorithms. Our work develops algorithms to examine x-ray images of paintings, not to analyze the artist's
brushstrokes but to characterize the weave of the canvas that supports the painting. The physics of radiography indicates
that linear processing of the x-rays is most appropriate. Our spectral analysis algorithms have an accuracy superior to
human spot-measurements and have the advantage that, through "short-space" Fourier analysis, they can be readily applied
to entire x-rays. We have found that variations in the manufacturing process create a unique pattern of horizontal and
vertical thread density variations in the bolts of canvas produced. In addition, we measure the thread angles, providing
a way to determine the presence of cusping and to infer the location of the tacks used to stretch the canvas on a frame
during the priming process. We have developed weave matching software that employs a new correlation measure to find
paintings that share canvas weave characteristics. Using a corpus of over 290 paintings attributed to Vincent van Gogh, we
have found several weave match cliques that we believe will refine the art historical record and provide more insight into
the artist's creative processes.
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