Smart-phone-assisted digital rejuvenation of medieval paintings

Project Summary

Liuyi Zhu (Computer Science, Math), Gilad Amitai (Masters, Statistics), Raphael Kim (Computer Science, Mechanical Engineering), and Andreas Badea (East Chapel Hill High School) spent ten weeks streamlining and automating the process of electronically rejuvenating medieval artwork. They used a 14th-century altarpiece by Francescussio Ghissi as a working example.

Themes and Categories
Interdisciplinary Inquiry
Data+, Digital Humanities, Social Sciences
Year
2017
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: The team built a prototype app which transforms the original image, using a combination of techniques including color segmentation, crack detection and inpainting, color mapping, punchmark detection, and gilding, They have distributed the app to conservators at the North Carolina Museum of Art for initial testing.

This project is inspired by the experience of the Duke Bass Connections team that developed the virtual rejuvenation of the Ghissi altarpiece, which went on display at the North Carolina Museum of Art.

Sponsored by Bass Connections

Click here for the Executive Summary

Faculty Lead: Ingrid Daubechies

Project Managers: Robert Ravier, Bruno Cornelis

Related People

Related News

Rejuvenating Art With Data
Tuesday, August 8, 2017
A Data+ team led by Ingrid Daubechies explored the feasibility of building an app that museum v ...

Related Projects

Queens of Antiquity

Understanding how to generate, analyze, and work with datasets in the humanities is often a difficult task without learning how to code or program. In humanities centered courses, we often privilege close reading or qualitative analysis over other methods of knowing, but by learning some new quantitative techniques we better prepare the students to tackle new forms of reading. This class will work with the data from the HathiTrust to develop ideas for thinking about how large groups and different discourse communities thought of queens of antiquity like Cleopatra and Dido.

Getting Granular on Social Determinants of Health

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

Investigating oil and gas production in the United Kingdom

Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. This requires historical data on bid history to investigate what leads to an increase in the number of (a) leases acquired and (b) companies participating in auctions. The goal of this team is to create a well-structured dataset based on company bid history from the U.K. Oil and Gas Authority; data which will come from many different file structures and formats (tabular, pdf, etc.). The team will curate these data to create a single, tabular database of U.K. bid history and work programs.

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov