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Computer vision and Archaeology

Computer vision and Archaeology. RICH Reading 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.

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Computer vision and Archaeology

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  1. Computer visionandArchaeology RICHReading Images for the Cultural Heritage

  2. 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)

  3. 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

  4. Working examples • Content-based image retrieval of historical glass • Incorporated in historical glass reference collection website • Automatic coin classification

  5. 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

  6. 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

  7. Current work...

  8. 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

  9. Automatic coin classification • Example coin (1 of 109 coin classes)

  10. Automatic coin classification • Using various contour features and texture features: • Edge-based statistical features • Gabor-based features • Daubechies wavelet features

  11. 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

  12. Automatic coin classification • Final goal: classification of medieval coins

  13. 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

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