master Assignment
Heritage Biometrics – Possibilities with AI in Furniture & Wood Conservation
Type: Master EE/CS/AM
Student: Unassigned
Duration: TBD (min. 6 months/fulltime)
If you are interested please contact:
Background:
A large 17-th century chimney piece and mantel designed by architect Philips Vinckboons (II) for a Dutch home (Tegelwerk van de schoorsteen Huydecoperhuis - Rijksmuseum) serves as an example of an object that the Furniture Conservation Department at the Rijksmuseum conducts research and restoration on. In their daily practice, conservators study physical evidence, such as toolmarks, fixings, and surface coatings to inform the understanding and treatment of objects. In addition to large architectural features, they work on a range of historic furniture and have been particularly interested in the beautiful marquetry patterns, often in the shape of birds, flowers, and butterflies, found on 17th and 18th century furniture and how these design patterns might be used to relate objects to one another.
Goals:
- Recognition of toolmarks, fixings, surface coatings on Dutch furniture.
- Recognition of marquetry pattern designs (i.e., birds, flowers, butterflies).
Supervisor and main contact: Dzemila Sero (assistant professor DMB)
Co-supervisor:
- Dana Melchar (furniture conservator, Rijksmuseum)
- dr. Doina Bucur (assistant professor, DMB-EEMCS)
Why join?
- Work at the intersection of conservation, art history and computer vision to make an impact in the new field of Heritage Biometrics.
- Collaborate with a team of interdisciplinary experts of engineers, conservators and computer scientists.
Who Should Apply?
You are a graduate student with experience in Computer Vision and Machine Learning / Artificial Intelligence. You should be a capable programmer with prior experience in using Python. The project requires critical thinking and exploring new directions, so you will also have the opportunity to go beyond current approaches. Affinity or interest in the world of decorative arts is expected. Enthusiasts of computer vision for decorative arts, seeking to make a tangible impact in heritage science.