US researchers are harnessing digital technology to help differentiate
original works of art from forgeries.
Penn State scientists James Z Wang, associate professor of information
sciences and technology, and Jia Li, associate professor of statistics, have
published their work in the July issue of IEEE Signal Processing.
The team's findings are based on 101 high-resolution greyscale scans of
paintings by Vincent van Gogh provided by the Van Gogh and Kröller-Müller
museums in the Netherlands.
The scientists broke each scan into sections measuring 512 x 512 pixels, or
about 2.5in x 2.5in in canvas size, and analysed the patterns and geometric
characteristics of the brush strokes.
From the 101 scans received from the museums, art historians identified 23 as
unquestionably authentic van Gogh works. These were used by the computer system
as a training database for the artist's brushstroke styles.
Statistical models were created to capture the unique style, or "handwriting
", that became the artist's signature in 23 of the scans.
The other 78 paintings, which were by van Gogh, van Gogh's peers or had at
one time been attributed to van Gogh but later found to be unauthentic, were
compared against the generated models to test the algorithms.
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Print-on-Demand and Grand Tour highlight success 23 May 2008
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