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.
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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
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