Robert Erdmann - Keynote - Python for Imaging and Artificial Intelligence in Cultural Heritage

For many people, a museum is the last place they would expect to find cutting-edge data science, but the world of cultural heritage is full of fascinating challenges for imaging and computation. The availability of high-resolution imaging, high-speed internet, and modern computational tools allows us to image cultural heritage objects in staggering detail and with a wide array of techniques. The result, though, is a data deluge: studying single objects like Rembrandt's Night Watch can generate terabytes of data, and there are millions of objects in the world's museums.

The huge Python ecosystem enables us to build tools to process, analyze, and visualize these data. Examples include creating the 717 gigapixel (!) image of the Night Watch and reconstructing the painting's long-lost missing pieces using AI; controlling a camera and automated turntable in Jupyter for 3D object photography; revealing hidden watermarks in works on paper using a hybrid physics and deep learning-based ink-removal model; using chemical imaging and convolutional neural networks to see the hidden structure of Rembrandt and Vermeer paintings; and using a webcam or smartphone camera to do real-time similarity search over a database of 2.3 million open-access cultural heritage images at 4 frames per second.

These and several other live demonstrations show how Python is essential in our work to help the world access, preserve, and understand its cultural heritage.

For many people, a museum is the last place they would expect to find cutting-edge data science, but the world of cultural heritage is full of fascinating challenges for imaging and computation. The availability of high-resolution imaging, high-speed internet, and modern computational tools allows us to image cultural heritage objects in staggering detail and with a wide array of techniques. The result, though, is a data deluge: studying single objects like Rembrandt's Night Watch can generate terabytes of data, and there are millions of objects in the world's museums.

The huge Python ecosystem enables us to build tools to process, analyze, and visualize these data. Examples include creating the 717 gigapixel (!) image of the Night Watch and reconstructing the painting's long-lost missing pieces using AI; controlling a camera and automated turntable in Jupyter for 3D object photography; revealing hidden watermarks in works on paper using a hybrid physics and deep learning-based ink-removal model; using chemical imaging and convolutional neural networks to see the hidden structure of Rembrandt and Vermeer paintings; and using a webcam or smartphone camera to do real-time similarity search over a database of 2.3 million open-access cultural heritage images at 4 frames per second.

These and several other live demonstrations show how Python is essential in our work to help the world access, preserve, and understand its cultural heritage.

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