Publikation: Autocompletion of repetitive stroking with image guidance
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present an assisted drawing system to autocomplete repetitive short strokes during a user’s normal drawing process. Users draw over a reference image as usual; at the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow the user’s input style when certain repetition is detected. Users can accept, modify, or ignore the system’s predictions and continue drawing, thus maintaining fluid control over drawing. Our key idea is to jointly analyze image regions and user input history to detect and predict repetition. The proposed system can effectively reduce the user’s workload when drawing repetitive short strokes, helping users to create results with rich patterns.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
CHEN, Yilan, Kin Chung KWAN, Hongbo FU, 2023. Autocompletion of repetitive stroking with image guidance. In: Computational Visual Media. Springer. 2023, 9(3), pp. 581-596. ISSN 2096-0433. eISSN 2096-0662. Available under: doi: 10.1007/s41095-022-0288-2BibTex
@article{Chen2023-03-08Autoc-66568, year={2023}, doi={10.1007/s41095-022-0288-2}, title={Autocompletion of repetitive stroking with image guidance}, number={3}, volume={9}, issn={2096-0433}, journal={Computational Visual Media}, pages={581--596}, author={Chen, Yilan and Kwan, Kin Chung and Fu, Hongbo} }
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/123456789/66568"> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:language>eng</dc:language> <dcterms:title>Autocompletion of repetitive stroking with image guidance</dcterms:title> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66568/1/Chen_2-nk0vq634s3384.pdf"/> <dc:creator>Fu, Hongbo</dc:creator> <dc:contributor>Chen, Yilan</dc:contributor> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:rights>Attribution 4.0 International</dc:rights> <dc:creator>Chen, Yilan</dc:creator> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/66568"/> <dcterms:abstract>Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present an assisted drawing system to autocomplete repetitive short strokes during a user’s normal drawing process. Users draw over a reference image as usual; at the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow the user’s input style when certain repetition is detected. Users can accept, modify, or ignore the system’s predictions and continue drawing, thus maintaining fluid control over drawing. Our key idea is to jointly analyze image regions and user input history to detect and predict repetition. The proposed system can effectively reduce the user’s workload when drawing repetitive short strokes, helping users to create results with rich patterns.</dcterms:abstract> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66568/1/Chen_2-nk0vq634s3384.pdf"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-04-13T07:59:04Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-04-13T07:59:04Z</dcterms:available> <dc:contributor>Kwan, Kin Chung</dc:contributor> <dc:contributor>Fu, Hongbo</dc:contributor> <dc:creator>Kwan, Kin Chung</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:issued>2023-03-08</dcterms:issued> </rdf:Description> </rdf:RDF>