Data paints a picture
Big Data mastery helps study and conserve Old Masters.
14 February 2020
Dr Miguel Rodrigues of UCL’s Department of Electronic and Electrical Engineering is working in partnership with the National Gallery to use cutting edge data science, machine learning and digital signal processing technology tools to support art conservation of Old Masters paintings.
The work was funded by the EPSRC and brings together ICT and Heritage Science researchers to enable the cross-pollination of ideas and expertise applied to art investigation.
The heritage science sector is experiencing a digital revolution linked to the emergence and increasing adoption of cutting-edge non-invasive analytical imaging techniques generating large volumes of multidimensional data from cultural heritage objects.
These include macro X-Ray fluorescence (MA-XRF) scanning and hyperspectral imaging (HSI). In particular, the tools that Dr Rodrigues is developing will provide the means to identify, characterise and visualise materials present within a painting, thereby leading to new insights relevant for the conservation and preservation of Old Master paintings.
For example, new tools will help visualise in a more integrated and accessible form the features of interest for art-historical study and conservation, such as underdrawing, pentimenti, concealed designs, losses or non-original materials.
To develop such tools, Dr Rodrigues’ research programme will include the development of multimodal signal processing machine learning algorithms for data correction, alignment, registration and mosaicking; and new multimodal signal analysis algorithms capable of inferring material distributions in a painting from MA-XRF and HSI data.
The Heritage Science sector will benefit from new automated, accessible, robust, user-friendly tools to aid the work of heritage scientists, art historians, and conservators.
Finally, the tools, images and insights they create will provide galleries with new innovative means to interpret and present their collections to the public.
- Find out more at ee.ucl.ac.uk/research