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For art investigations of paintings, multiple imaging technologies, such asvisual light photography, infrared reflectography, ultraviolet fluorescencephotography, and x-radiography are often used. For a pixel-wise comparison, themulti-modal images have to be registered. We present a registration andvisualization software tool, that embeds a convolutional neural network toextract cross-modal features of the crack structures in historical paintingsfor automatic registration. The graphical user interface processes the user'sinput to configure the registration parameters and to interactively adapt theimage views with the registered pair and image overlays, such as by individualor synchronized zoom or movements of the views. In the evaluation, wequalitatively and quantitatively show the effectiveness of our software tool interms of registration performance and short inference time on multi-modalpaintings and its transferability by applying our method to historical prints.

A Multi-modal Registration and Visualization Software Tool for Artworks using CraquelureNet