Computer vision and Archaeology RICH Reading Images for the Cultural Heritage.

Computer visionand

Archaeology

RICHReading Images for the Cultural Heritage

RICH team

• Guus Lange (ROB, Amersfoort)

• Eric Postma (MICC-IKAT, UM)

• Paul Boon (MICC-IKAT, UM)

• Hans Paijmans (MICC-IKAT, UM)

• Laurens van der Maaten (MICC-IKAT, UM)

RICH aims

• Development of new techniques for automatic image analysis

• Providing tools to archaeology to make classification easier, faster, and more objective

• Enrichment of knowledge on archaeological material

Working examples

• Content-based image retrieval of historical glass – Incorporated in historical glass reference

collection website

• Automatic coin classification

CBIR for historical glass

• Aids classification of glass– Nowadays, the expert searches through entire

books to find ‘alike’ glass drawings– This process is slow and error-prone

• Our system compares glass photographs (made by the expert) with glass drawings (from the books)

• Provides entry into collection website

CBIR for historical glass

• Allows for knowledge enrichment– All objects in the collection can be compared– Visualization of this comparison allow insight

in relations between objects– Unsupervised learning could even be used to

construct new typologies

Current work...

Automatic coin classification

• After introduction of the euro, large amounts of unsorted coins were collected (over 300 tons)

• Manual sorting not feasible

• We are developing a high-performance, high-speed system for coin classification

Automatic coin classification

• Example coin (1 of 109 coin classes)

Automatic coin classification

• Using various contour features and texture features:– Edge-based statistical features– Gabor-based features– Daubechies wavelet features

Automatic coin classification

• Our system achieves promising classification performances (currently ~76%)

• Rejecting unknown or unclear coins (low number of wrong classifications)

• Classification takes 1 second on a normal desktop PC– Including image loading, segmentation, feature

extraction and classification

Automatic coin classification

• Final goal: classification of medieval coins

Conclusions

• RICH delivers useful applications to archaeology

• RICH delivers new insights– To archaeologists:

• New view on typologies and classifications

– To computer scientists: • Provides difficult, real-world data for the

development of new techniques