Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans

TL;DR

This paper proposes a computer vision approach to analyze how artists create focal points in paintings by comparing original images with their painted versions, focusing on edge differences to understand artistic intent.

Contribution

It introduces a novel methodology combining alignment and edge detection to identify centres of interest in paintings, providing insights into artistic transformation processes.

Findings

Identified key focal areas in paintings through edge difference analysis.

Demonstrated the methodology on works by Luc Tuymans.

Provided a framework for further research in art analysis and education.

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.