Robotic Painting using Semantic Image Abstraction

Stroh, Michael; Paetzold, Patrick; Berio, Daniel; Leymarie, Frederic Fol; Kehlbeck, Rebecca and Deussen, Oliver. 2025. 'Robotic Painting using Semantic Image Abstraction'. In: ACM/EG Expressive Symposium - WICED: Eurographics Workshop on Intelligent Cinematography and Editing - Artworks, Posters, Demos. London, United Kingdom 11 - 12 May 2025. [Conference or Workshop Item]

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Abstract or Description

We 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.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.2312/exw.20251070

Keywords:

Computing methodologies, Non-photorealistic rendering, Image processing

Related URLs:

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
2025Accepted
May 2025Published

Event Location:

London, United Kingdom

Date range:

11 - 12 May 2025

Item ID:

39494

Date Deposited:

05 Sep 2025 13:09

Last Modified:

08 Sep 2025 12:33

URI:

https://research.gold.ac.uk/id/eprint/39494

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