Aligning non-exact copies of artworks with their original

This research investigates the efficacy and reliability of geometric matching for the specific case of aligning non-exact copies of artistic works with the original from which they were derived. The purpose of which is to provide a foundation for comparison in any further analysis conducted by conservators and art historians. An overview of the...

Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM: Application to Chest CT Scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: 'correct' 0-3 mm, 'poor' 3-6 mm and 'wrong' over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where...