Stroh, MichaelPaetzold, PatrickBerio, DanielLeymarie, Frederic FolKehlbeck, RebeccaDeussen, OliverBerio, DanielBruckert, Alexandre2025-05-092025-05-092025978-3-03868-272-1https://doi.org/10.2312/exw.20251070https://diglib.eg.org/handle/10.2312/exw20251070We present a novel image segmentation and abstraction pipeline tailored to robot painting applications. We address the unique challenges of realizing digital abstractions as physical artistic renderings. Our approach generates adaptive, semantics-based abstractions that balance aesthetic appeal, structural coherence, and practical constraints inherent to robotic systems. By integrating panoptic segmentation with color-based over-segmentation, we partition images into meaningful regions corresponding to semantic objects while providing customizable abstraction levels we optimize for robotic realization. We employ saliency maps and color difference metrics to support automatic parameter selection to guide a merging process that detects and preserves critical object boundaries while simplifying less salient areas. Graph-based community detection further refines the abstraction by grouping regions based on local connectivity and semantic coherence. These abstractions enable robotic systems to create paintings on real canvases with a controlled level of detail and abstraction.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Non-photorealistic rendering; Image processingComputing methodologies → Nonphotorealistic renderingImage processing10.2312/exw.202510704 pages