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CraquelureNet: Matching the Crack Structure in Historical Paintings for Multi-Modal Image Registration

Citation Author(s):
Aline Sindel, Andreas Maier, and Vincent Christlein
Submitted by:
Aline Sindel
Last updated:
29 September 2021 - 10:50am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Aline Sindel
Paper Code:
MLR-APPL-IP-8.6

Abstract 

Abstract: 

Visual light photography, infrared reflectography, ultraviolet fluorescence photography and x-radiography reveal even hidden compositional layers in paintings. To investigate the connections between these images, a multi-modal registration is essential. Due to varying image resolutions, modality dependent image content and depiction styles, registration poses a challenge. Historical paintings usually show crack structures called craquelure in the paint. Since craquelure is visible by all modalities, we extract craquelure features for our multi-modal registration method using a convolutional neural network. We jointly train our keypoint detector and descriptor using multi-task learning. We created a multi-modal dataset of historical paintings with keypoint pair annotations and class labels for craquelure detection and matching. Our method demonstrates the best registration performance on the multi-modal dataset in comparison to competing methods.

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