Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Hemani, Mayur | |
dc.contributor.author | Sinha, Abhishek | |
dc.contributor.author | Krishnamurthy, Balaji | |
dc.contributor.editor | Skala, Václav | |
dc.date.accessioned | 2019-10-22T09:13:21Z | |
dc.date.available | 2019-10-22T09:13:21Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | WSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 37-43. | en |
dc.identifier.isbn | 978-80-86943-37-4 (CD/-ROM) | |
dc.identifier.issn | 2464–4617 (print) | |
dc.identifier.issn | 2464-4625 (CD/DVD) | |
dc.identifier.uri | http://hdl.handle.net/11025/35607 | |
dc.format | 7 s. | cs |
dc.format.mimetype | application/odt | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.rights | © Václav Skala - UNION Agency | cs |
dc.subject | neuronové sítě | cs |
dc.subject | manipulace s obrázky | cs |
dc.subject | stylizace obrazu | cs |
dc.subject | skica | cs |
dc.title | Stylized Sketch Generation using Convolutional Networks | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | The task of synthesizing sketches from photographs has been pursued with image processing methods and supervised learning based approaches. The former lack flexibility and the latter require large quantities of ground-truth data which is hard to obtain because of the manual effort required. We present a convolutional neural network based framework for sketch generation that does not require ground-truth data for training and produces various styles of sketches. The method combines simple analytic loss functions that correspond to characteristics of the sketch. The network is trained on and evaluated for human face images. Several stylized variations of sketches are obtained by varying the parameters of the loss functions. The paper also discusses the implicit abstraction afforded by the deep convolutional network approach which results in high quality sketch output. | en |
dc.subject.translated | neural-networks | en |
dc.subject.translated | image manipulation | en |
dc.subject.translated | image stylization | en |
dc.subject.translated | sketch style | en |
dc.identifier.doi | https://doi.org/10.24132/CSRN.2019.2901.1.5 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2019: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
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Hemani.pdf | Plný text | 7,3 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/35607
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