Název:	Stylized Sketch Generation using Convolutional Networks
Autoři:	Hemani, Mayur Sinha, Abhishek Krishnamurthy, Balaji
Citace zdrojového dokumentu:	WSCG 2019: full papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 37-43.
Datum vydání:	2019
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/35607
ISBN:	978-80-86943-37-4 (CD/-ROM)
ISSN:	2464–4617 (print) 2464-4625 (CD/DVD)
Klíčová slova:	neuronové sítě;manipulace s obrázky;stylizace obrazu;skica
Klíčová slova v dalším jazyce:	neural-networks;image manipulation;image stylization;sketch style
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2019: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.