Abstract
What is the creative process through which an artist goes from an original image to a painting? Can we examine this process using techniques from computer vision and pattern recognition? Here we set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establish centres of interest, which are focal areas of a painting that carry meaning. We introduce a comparative methodology that first cuts out the minimal segment from the original image on which the painting is based, then aligns the painting with this source, investigates micro-differences to identify centres of interest and attempts to understand their role. In this paper we focus exclusively on micro-differences with respect to edges. We believe that research into where and how artists create centres of interest in paintings is valuable for curators, art historians, viewers, and art educators, and might even help artists to understand and refine their own artistic method.
This work was made possible thanks to a ‘scientist in residence of an artist studio’ grant to Luc Steels and Studio Luc Tuymans. The residency was funded by the European Commission’s S+T+ARTS programme, set up by DG CONNECT. It was organized by BOZAR, a Belgian art centre located in Brussels, and GLUON, a Brussels organisation facilitating art-science interactions. Additional funding for this research has come from the H2020 EU project MUHAI on Meaning and Understanding in AI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)
de Boer, V., et al.: Amsterdam museum linked open data. Semantic Web 4(3), 237–243 (2013)
Cetinica, E., Lipica, T., Grgicb, S.: Fine-tuning convolutional neural networks for fine art classification. Expert Syst. Appl. 114, 107–118 (2018)
Corenlis, B.: Image processing for art investigation. Electron. Lett. Comput. Vis. Image Anal. 14(3), 13–15 (2015)
Dewey, R.: Hack the Experience. New Tools for Artists from Cognitive Science. Brainstorm Books, Punctum Books, Goleta Ca (2018)
Donnadieu, M.: La Pelle, exhibition guide. Palazzo Grassi, Venice (2019)
Donoser, M., Bishof, H.: Efficient maximally stable extremal region (MSER) tracking. In: Proceedings of the 2006 Conference on Computer Vision and Pattern recognition (CVPR). Computer Vision Foundation (2006)
Fredrickson, B., Roberts, T.A.: Objectification theory. Toward understanding women’s lived experiences and mental health risks. Psychol. Women Q. 21, 173–206 (1997)
Goshtasby, A.A.: 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. Wiley, New York (2005)
Kroner, A., Senden, M., Driessens, K., Goebel, R.: Contextual encoder-decoder network for visual saliency prediction. Neural Netw. 129, 261–270 (2020)
Ledoux, J.: The Emotional Brain. Simon and Schuster, New york (1996)
Meyer-Hermann, E.: Catalogue Raisonné of Paintings by Luc Tuymans. David Zwirner Books, Yale University Press, New Haven (2019)
Panofsky, E.: Studies in Iconology. Humanistic Themes in the Art of the Renaissance, p. 1972. Oxford University Press, Oxford (1939)
Rahunathan, S., Stredney, D., Schmalbrock, P., Clymer, B.D.: Image registration using rigid registration and maximization of mutual information. In: 13th Annual Medicine Meets Virtual Reality (2005)
Semmo, A., Isenberg, T., Doellner, J.: Neural style transfer: a paradigm shift for image-based artistic rendering? In: Proceedings of the Symposium on Non-Photorealistic Animation and Rendering. ACM (2017)
Sobel, I., Feldman, G.: A 3\(\times \)3 isotropic gradient operator for image processing. A talk at the Stanford Artificial Project, pp. 271–272 (1968)
Steels, L., Wahle, B.: Perceiving the focal point of a painting with AI. Case studies on works of Luc Tuymans. In: 12th International Conference on Agents and Artificial Intelligence. Scite Press, Setubal, Portugal (2020)
Styner, M., Brechbuhler, C., Szckely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. Med. Imaging 19(3), 153–165 (2000)
Tuymans, L.: The Image Revisited. In Conversation with Boehm, G., Clark, T., and De Wolf, H. Ludion, Brussels (2018)
Loock, U., Aliaga, J.-V., Spector, N.: Luc Tuymans. Phaidon, London (1996)
Vilarroya, O., i Argemon, F.F. (eds): Social Brain Matters: Stances on the Neurobiology of Social Cognition. Brill, Amsterdam (2007)
Wibisono, K., Hang, H.M.: Traditional method inspired deep neural network for edge detection. In: IEEE International Conference on Image Processing (ICIP), pp. 678–682 (2020)
Wilson, G., Cook, D.J.: A survey of unsupervised deep domain adaptation. ACM Trans. Intell. Syst. Techn. (TIST) 11(5), 1–46 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Aslan, S., Steels, L. (2021). Identifying Centres of Interest in Paintings Using Alignment and Edge Detection. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-68796-0_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68795-3
Online ISBN: 978-3-030-68796-0
eBook Packages: Computer ScienceComputer Science (R0)
