A proof of concept for aligning sketches to their corresponding painting

Bachelor Thesis (2022)
Author(s)

M.E. Radder (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Ruben Wiersma – Mentor (TU Delft - Computer Graphics and Visualisation)

R. Marroquim – Graduation committee member (TU Delft - Computer Graphics and Visualisation)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Marit Radder
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Marit Radder
Graduation Date
23-06-2022
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Aligning sketches to their corresponding painting could give more insight into the creative process of an artist. This is a difficult task that cannot be solved directly with classical image registration techniques. Typically, features such as cracks and brushstrokes are used to match different modalities. A sketch does not contain such artifacts and is often roughly similar to the final painting. Therefore the following question rises what suitable feature detection, feature extraction/description, and transform model estimation methods can be used to align sketches to their corresponding painting. This paper provides a proof of concept by taking a manual feature detection, and a histogram of orientations as a feature description. We demonstrate that our algorithm can automatically align sketches with up to 1.4 percent of the accuracy of manual alignment.

Files

License info not available