Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization

We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the enti...
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Gespeichert in:

Autor*in:	Lijie Yang [verfasserIn] Tianchen Xu [verfasserIn] Jixiang Du [verfasserIn] Enhua Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 35449-35456
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:35449-35456

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2904669

Katalog-ID:	DOAJ016042891

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520			\|a We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users.
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spelling	10.1109/ACCESS.2019.2904669 doi (DE-627)DOAJ016042891 (DE-599)DOAJ69ea8964a6504e53baf9fafcf2fbb17a DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users. Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting Electrical engineering. Electronics. Nuclear engineering Tianchen Xu verfasserin aut Jixiang Du verfasserin aut Enhua Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 35449-35456 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:35449-35456 https://doi.org/10.1109/ACCESS.2019.2904669 kostenfrei https://doaj.org/article/69ea8964a6504e53baf9fafcf2fbb17a kostenfrei https://ieeexplore.ieee.org/document/8666126/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 35449-35456
allfields_unstemmed	10.1109/ACCESS.2019.2904669 doi (DE-627)DOAJ016042891 (DE-599)DOAJ69ea8964a6504e53baf9fafcf2fbb17a DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users. Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting Electrical engineering. Electronics. Nuclear engineering Tianchen Xu verfasserin aut Jixiang Du verfasserin aut Enhua Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 35449-35456 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:35449-35456 https://doi.org/10.1109/ACCESS.2019.2904669 kostenfrei https://doaj.org/article/69ea8964a6504e53baf9fafcf2fbb17a kostenfrei https://ieeexplore.ieee.org/document/8666126/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 35449-35456
allfieldsGer	10.1109/ACCESS.2019.2904669 doi (DE-627)DOAJ016042891 (DE-599)DOAJ69ea8964a6504e53baf9fafcf2fbb17a DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users. Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting Electrical engineering. Electronics. Nuclear engineering Tianchen Xu verfasserin aut Jixiang Du verfasserin aut Enhua Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 35449-35456 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:35449-35456 https://doi.org/10.1109/ACCESS.2019.2904669 kostenfrei https://doaj.org/article/69ea8964a6504e53baf9fafcf2fbb17a kostenfrei https://ieeexplore.ieee.org/document/8666126/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 35449-35456
allfieldsSound	10.1109/ACCESS.2019.2904669 doi (DE-627)DOAJ016042891 (DE-599)DOAJ69ea8964a6504e53baf9fafcf2fbb17a DE-627 ger DE-627 rakwb eng TK1-9971 Lijie Yang verfasserin aut Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users. Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting Electrical engineering. Electronics. Nuclear engineering Tianchen Xu verfasserin aut Jixiang Du verfasserin aut Enhua Wu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 35449-35456 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:35449-35456 https://doi.org/10.1109/ACCESS.2019.2904669 kostenfrei https://doaj.org/article/69ea8964a6504e53baf9fafcf2fbb17a kostenfrei https://ieeexplore.ieee.org/document/8666126/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 35449-35456
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source	In IEEE Access 7(2019), Seite 35449-35456 volume:7 year:2019 pages:35449-35456
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authorswithroles_txt_mv	Lijie Yang @@aut@@ Tianchen Xu @@aut@@ Jixiang Du @@aut@@ Enhua Wu @@aut@@
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callnumber-first	T - Technology
author	Lijie Yang
spellingShingle	Lijie Yang misc TK1-9971 misc Interactive drawing misc stroke generation misc artistic stylization misc non-photorealistic rendering misc Chinese flower painting misc Electrical engineering. Electronics. Nuclear engineering Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization
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topic_title	TK1-9971 Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization Interactive drawing stroke generation artistic stylization non-photorealistic rendering Chinese flower painting
topic	misc TK1-9971 misc Interactive drawing misc stroke generation misc artistic stylization misc non-photorealistic rendering misc Chinese flower painting misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Interactive drawing misc stroke generation misc artistic stylization misc non-photorealistic rendering misc Chinese flower painting misc Electrical engineering. Electronics. Nuclear engineering
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title	Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization
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title_sort	easy drawing: generation of artistic chinese flower painting by stroke-based stylization
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title_auth	Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization
abstract	We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users.
abstractGer	We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users.
abstract_unstemmed	We propose a new photo-guided drawing tool for the easy generation of artistic strokes in the style of flower painting, which is the most representative one in Chinese paintings, through stylizing rough sketch lines over a photoimage. While existing style migration methods aim at processing the entire image, we depict that a stroke-based stylization framework better performs detailed style features and catches the user's intent. The steps in our technique are first to correct user inaccurate strokes that mark candidates of style patterns on a painting and skeletal paths to be stylized over a photograph, then to select the optimal pattern for every skeletal stroke, and finally, to map the pattern onto skeletal stroke by shape calculation and texture synthesis. This tool can tolerate errors from user inputs and work well for touch devices. Our experiments prove that the paintings our technique helped to generate show close to the specified style and are aesthetically satisfied by normal users.
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title_short	Easy Drawing: Generation of Artistic Chinese Flower Painting by Stroke-Based Stylization
url	https://doi.org/10.1109/ACCESS.2019.2904669 https://doaj.org/article/69ea8964a6504e53baf9fafcf2fbb17a https://ieeexplore.ieee.org/document/8666126/ https://doaj.org/toc/2169-3536
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