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Machine Learning and Image Processing for Art Investigation Research Group



With the advent of new data acquisition technologies, it is becoming increasingly common for art museums to compile digital archives of their collections for internal use. These records can include extremely high resolution images of paintings or drawings under visible light, X-rays, multispectral images (from which local paint pigment composition information can be derived), or even laser scans of a paintings three-dimensional surface. However, thus far, little attempt has been made to subject this wealth of data to sophisticated computational analyses, even though such analyses could potentially address a variety of questions of great art historical interest.

Having backgrounds from different disciplines like Applied Mathematics, Computer Science and Electrical Engineering, we are using state of the art machine learning and signal processing techniques to extract useful information from digital scans of art works.



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