Computer vision and machine learning for archaeology The potential of machine learning and image analysis techniques for the domain of archaeology Drs. L. van der Maaten, Drs. P.J. Boon, Dr. A.G. Lange, Dr. H. Paijmans, Prof.dr. E.O. Postma Paul Boon Institute for Knowledge and Agent Technology Maastricht University

Outline Introduction –Why & How –Computer vision and machine learning Examples –Historical glass, on-line reference collection –Modern coins Conclusion Future work

Why & How Need for on-line reference collections Making a collection digital is not enough Create tools for identifying artifacts –Vision and machine learning for finding ‘alike’ objects –Web technology for presentation and navigation Collection Artifact? Library

Computer vision and machine learning 186, gl_flu_4

Computer vision and machine learning 186, gl_flu_4 1.Object detection 2.Feature extraction 3.Classification

Computer vision and machine learning 186, gl_flu_4 Not a replacement, but a tool!

Example Historical glass Content based image retrieval Outer shape contours

Object detection

Shape context r θ θ r Translation, scale and rotation invariant Histogram construction

Classification

Example Coins Automatic classification system, using texture information Modern coins, but in future early- medieval coins

Original

Object detection

Features

Conclusion You can successfully integrate computer vision and machine learning with an online collection

Future work Improve usefulness –Object detection –Evaluate technology Other types of material –Using outer shape and texture Partial shapes, from sherds? Evaluate typologies or device new ones?

Shape map

Acknowledgements We thank the Dutch Royal Coin Cabinet for providing the medieval coin dataset Arent Pol – specialist on early-medieval coins Institute for Knowledge and Agent Technology, Maastricht University Dutch National Service for Archaeological Heritage Jaap Kottman – glass specialist Netherlands Organization for Scientific Research