WebPainter: Collaborative Stroke-Based Rendering Through HTML5 and WebGL

Xie, N; Ren, M; Yang, W; Yang, Y; Shen, HT

WebPainter: Collaborative Stroke-Based Rendering Through HTML5 and WebGL

Xie, N Ren, M Yang, W Yang, Y Shen, HT

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10345 LNCS pp. 210 - 217
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Xie, N	en_US
dc.contributor.author	Ren, M	en_US
dc.contributor.author	Yang, W	en_US
dc.contributor.author	Yang, Y	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10345 LNCS pp. 210 - 217	en_US
dc.identifier.isbn	9783319658483	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127375
dc.description.abstract	© 2017, Springer International Publishing AG. Computer-aided drawing system assists users to convert the input real photos into painterly style images. Nowadays, it is widely developed as cloud brush engine service in many creative software tools and applications of artistic rendering such as Prisma [1], Photoshop [2], and Meitu [3], because the machine learning server has more powerful than the stand-alone version. In this paper, we propose a web collaborative Stroke-based Learning and Rendering (WebSBLR) system. Different from the existing methods that are mainly focused on the artistic filters, we concentrate on the stroke realistic rendering engine for browser on client using WebGL and HTML5. Moreover, we implement the learning-based stroke drawing path generation module on the server. By this way, we enable to achieve the computer-supported cooperative work (CSCW), especially for multi-screen synchronous interaction. The experiments demonstrated our method are efficient to web-based multi-screen painting simulation.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-65849-0_22	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	WebPainter: Collaborative Stroke-Based Rendering Through HTML5 and WebGL	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10345 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10345 LNCS	en_US

Abstract:

© 2017, Springer International Publishing AG. Computer-aided drawing system assists users to convert the input real photos into painterly style images. Nowadays, it is widely developed as cloud brush engine service in many creative software tools and applications of artistic rendering such as Prisma [1], Photoshop [2], and Meitu [3], because the machine learning server has more powerful than the stand-alone version. In this paper, we propose a web collaborative Stroke-based Learning and Rendering (WebSBLR) system. Different from the existing methods that are mainly focused on the artistic filters, we concentrate on the stroke realistic rendering engine for browser on client using WebGL and HTML5. Moreover, we implement the learning-based stroke drawing path generation module on the server. By this way, we enable to achieve the computer-supported cooperative work (CSCW), especially for multi-screen synchronous interaction. The experiments demonstrated our method are efficient to web-based multi-screen painting simulation.

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http://hdl.handle.net/10453/127375