Using Saliency for Semantic Image Abstractions in Robotic Painting

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dc.contributor.authorStroh, Michaelen_US
dc.contributor.authorPaetzold, Patricken_US
dc.contributor.authorBerio, Danielen_US
dc.contributor.authorKehlbeck, Rebeccaen_US
dc.contributor.authorLeymarie, Frederic Folen_US
dc.contributor.authorDeussen, Oliveren_US
dc.contributor.authorFaraj, Nouraen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.date.accessioned2025-10-07T05:02:59Z
dc.date.available2025-10-07T05:02:59Z
dc.date.issued2025
dc.description.abstractWe present an adaptive, semantics-based abstraction approach that balances aesthetic quality and structural coherence within the practical constraints of robotic painting. We apply panoptic segmentation with color-based over-segmentation to partition images into meaningful regions aligned with semantic objects, while providing flexible abstraction levels. Automatic parameter selection for region merging is enabled by semantic saliency maps, derived from Out-of-Distribution segmentation techniques in combination with machine learning methods for feature detection. This preserves the boundaries of salient objects while simplifying less prominent regions. A graph-based community detection step further refines the abstraction by grouping regions according to local connectivity and semantic coherence. The runtime of our method outperforms optimization-based image vectorization methods, enabling the efficient generation of multiple abstraction levels that can serve as hierarchical layers for robotic painting. We demonstrate the quality of our method by showing abstraction results, robotic paintings with the e-David robot, and a comparison to other abstraction methods.en_US
dc.description.number7
dc.description.sectionheadersStylization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70259
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70259
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70259
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Non-photorealistic rendering; Image processing; Applied computing → Fine arts
dc.subjectComputing methodologies → Non
dc.subjectphotorealistic rendering
dc.subjectImage processing
dc.subjectApplied computing → Fine arts
dc.titleUsing Saliency for Semantic Image Abstractions in Robotic Paintingen_US
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