Publikation:

Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting

Lade...
Vorschaubild

Dateien

Stroh_2-1g6y854gw9mhe3.pdf
Stroh_2-1g6y854gw9mhe3.pdfGröße: 27.36 MBDownloads: 22

Datum

2023

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Bookpart
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

GUTHE, Michael, Hrsg., Thorsten GROSCH, Hrsg.. VMV 2023: Vision, Modeling, and Visualization. Goslar: Eurographics Association, 2023, S. 133-140. ISBN 978-3-03868-232-5. Verfügbar unter: doi: 10.2312/vmv.20231235

Zusammenfassung

We propose a comprehensive pipeline for generating adaptable image abstractions from input pictures, tailored explicitly for robotic painting tasks. Our pipeline addresses several key objectives, including the ability to paint from background to foreground, maintain fine details, capture structured regions accurately, and highlight important objects. To achieve this, we employ a panoptic segmentation network to predict the semantic class membership for each pixel in the image. This step provides us with a detailed understanding of the object categories present in the scene. Building upon the semantic segmentation results, we combine them with a color-based image over-segmentation technique. This process partitions the image into monochromatic regions, each corresponding to a specific semantic object. Next, we construct a hierarchical tree based on the segmentation results, which allows us to merge adjacent regions based on their color difference and semantic class. We take care to ensure that shapes belonging to different semantic objects are not merged together. We iteratively perform adjacency merging until no further combinations are possible, resulting in a refined hierarchical shape tree. To obtain the desired image abstraction, we filter the hierarchical shape tree by examining factors such as color differences, relative sizes, and the layering within the hierarchy of each region in relation to their parent regions. By employing this approach, we can preserve fine details, apply local filtering operations, and effectively combine regions with structured shapes. This results in image abstractions well-suited for robotic painting applications and artistic renderings.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

VMV 2023: Vision, Modeling, and Visualization, 27. Sept. 2023 - 29. Sept. 2023, Braunschweig, Germany
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690STROH, Michael, Jörg Marvin GÜLZOW, Oliver DEUSSEN, 2023. Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting. VMV 2023: Vision, Modeling, and Visualization. Braunschweig, Germany, 27. Sept. 2023 - 29. Sept. 2023. In: GUTHE, Michael, Hrsg., Thorsten GROSCH, Hrsg.. VMV 2023: Vision, Modeling, and Visualization. Goslar: Eurographics Association, 2023, S. 133-140. ISBN 978-3-03868-232-5. Verfügbar unter: doi: 10.2312/vmv.20231235
BibTex
@inproceedings{Stroh2023Seman-70989,
  year={2023},
  doi={10.2312/vmv.20231235},
  title={Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting},
  isbn={978-3-03868-232-5},
  publisher={Eurographics Association},
  address={Goslar},
  booktitle={VMV 2023: Vision, Modeling, and Visualization},
  pages={133--140},
  editor={Guthe, Michael and Grosch, Thorsten},
  author={Stroh, Michael and Gülzow, Jörg Marvin and Deussen, Oliver}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/70989">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:abstract>We propose a comprehensive pipeline for generating adaptable image abstractions from input pictures, tailored explicitly for robotic painting tasks. Our pipeline addresses several key objectives, including the ability to paint from background to foreground, maintain fine details, capture structured regions accurately, and highlight important objects. To achieve this, we employ a panoptic segmentation network to predict the semantic class membership for each pixel in the image. This step provides us with a detailed understanding of the object categories present in the scene. Building upon the semantic segmentation results, we combine them with a color-based image over-segmentation technique. This process partitions the image into monochromatic regions, each corresponding to a specific semantic object. Next, we construct a hierarchical tree based on the segmentation results, which allows us to merge adjacent regions based on their color difference and semantic class. We take care to ensure that shapes belonging to different semantic objects are not merged together. We iteratively perform adjacency merging until no further combinations are possible, resulting in a refined hierarchical shape tree. To obtain the desired image abstraction, we filter the hierarchical shape tree by examining factors such as color differences, relative sizes, and the layering within the hierarchy of each region in relation to their parent regions. By employing this approach, we can preserve fine details, apply local filtering operations, and effectively combine regions with structured shapes. This results in image abstractions well-suited for robotic painting applications and artistic renderings.</dcterms:abstract>
    <dc:creator>Gülzow, Jörg Marvin</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting</dcterms:title>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Deussen, Oliver</dc:creator>
    <dc:contributor>Gülzow, Jörg Marvin</dc:contributor>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70989/4/Stroh_2-1g6y854gw9mhe3.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Stroh, Michael</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/70989"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-10-17T06:54:46Z</dc:date>
    <dc:contributor>Deussen, Oliver</dc:contributor>
    <dc:creator>Stroh, Michael</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-10-17T06:54:46Z</dcterms:available>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70989/4/Stroh_2-1g6y854gw9mhe3.pdf"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:issued>2023</dcterms:issued>
    <dc:language>eng</dc:language>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen