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The contour image style-transfer-based convolutional neural network. / Deng, Nan; Li, Jing; Wang, Xingce; Wu, Zhongke; Fu, Yan; Shui, Wuyang; Zhou, Mingquan; Korkhov, Vladimir; Gaspary, Luciano Paschoal.

International Workshop on Advanced Image Technology, IWAIT 2019. ed. / Q Kemao; K Hayase; PY Lau; WN Lie; YL Lee; S Srisuk; L Yu. SPIE, 2019. 110493C (Proceedings of SPIE; Vol. 11049).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Deng, N, Li, J, Wang, X, Wu, Z, Fu, Y, Shui, W, Zhou, M, Korkhov, V & Gaspary, LP 2019, The contour image style-transfer-based convolutional neural network. in Q Kemao, K Hayase, PY Lau, WN Lie, YL Lee, S Srisuk & L Yu (eds), International Workshop on Advanced Image Technology, IWAIT 2019., 110493C, Proceedings of SPIE, vol. 11049, SPIE, International Workshop on Advanced Image Technology 2019, IWAIT 2019, Singapore, Singapore, 6/01/19. https://doi.org/10.1117/12.2521489

APA

Deng, N., Li, J., Wang, X., Wu, Z., Fu, Y., Shui, W., Zhou, M., Korkhov, V., & Gaspary, L. P. (2019). The contour image style-transfer-based convolutional neural network. In Q. Kemao, K. Hayase, PY. Lau, WN. Lie, YL. Lee, S. Srisuk, & L. Yu (Eds.), International Workshop on Advanced Image Technology, IWAIT 2019 [110493C] (Proceedings of SPIE; Vol. 11049). SPIE. https://doi.org/10.1117/12.2521489

Vancouver

Deng N, Li J, Wang X, Wu Z, Fu Y, Shui W et al. The contour image style-transfer-based convolutional neural network. In Kemao Q, Hayase K, Lau PY, Lie WN, Lee YL, Srisuk S, Yu L, editors, International Workshop on Advanced Image Technology, IWAIT 2019. SPIE. 2019. 110493C. (Proceedings of SPIE). https://doi.org/10.1117/12.2521489

Author

Deng, Nan ; Li, Jing ; Wang, Xingce ; Wu, Zhongke ; Fu, Yan ; Shui, Wuyang ; Zhou, Mingquan ; Korkhov, Vladimir ; Gaspary, Luciano Paschoal. / The contour image style-transfer-based convolutional neural network. International Workshop on Advanced Image Technology, IWAIT 2019. editor / Q Kemao ; K Hayase ; PY Lau ; WN Lie ; YL Lee ; S Srisuk ; L Yu. SPIE, 2019. (Proceedings of SPIE).

BibTeX

@inproceedings{6b066eb4d26b414c9979bb92434b9a29,
title = "The contour image style-transfer-based convolutional neural network",
abstract = "The aim of style transfer is giving the style from one picture to another. The application of neural network in image processing separates the high level features and low level features of the image in the process of style transfer, and derives a variety of methods and optimization for style processing. The style transfer generates new images by separating and recombining the content and style of original images. In this process, various factors such as color and illumination will affect the result. The traditional algorithm only focuses on continuous pixels and the whole image, this paper will extend the process object to the contour of the image, and improves the detail processing from the existing style transfer examples. From the contour of images, the target image retains the contour feature of style image and the content of original image, in other word, gives the contour style of style image to original image. Finally, the style transfer effect based on the original image contour is obtained with some defects. The work can be easily extended to the aspects of video and 3D images.",
keywords = "contour processing, convolutional neural network, deep learning, style transfer",
author = "Nan Deng and Jing Li and Xingce Wang and Zhongke Wu and Yan Fu and Wuyang Shui and Mingquan Zhou and Vladimir Korkhov and Gaspary, {Luciano Paschoal}",
year = "2019",
month = jul,
day = "1",
doi = "10.1117/12.2521489",
language = "English",
series = "Proceedings of SPIE",
publisher = "SPIE",
editor = "Q Kemao and K Hayase and PY Lau and WN Lie and YL Lee and S Srisuk and L Yu",
booktitle = "International Workshop on Advanced Image Technology, IWAIT 2019",
address = "United States",
note = "International Workshop on Advanced Image Technology 2019, IWAIT 2019 ; Conference date: 06-01-2019 Through 09-01-2019",

}

RIS

TY - GEN

T1 - The contour image style-transfer-based convolutional neural network

AU - Deng, Nan

AU - Li, Jing

AU - Wang, Xingce

AU - Wu, Zhongke

AU - Fu, Yan

AU - Shui, Wuyang

AU - Zhou, Mingquan

AU - Korkhov, Vladimir

AU - Gaspary, Luciano Paschoal

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The aim of style transfer is giving the style from one picture to another. The application of neural network in image processing separates the high level features and low level features of the image in the process of style transfer, and derives a variety of methods and optimization for style processing. The style transfer generates new images by separating and recombining the content and style of original images. In this process, various factors such as color and illumination will affect the result. The traditional algorithm only focuses on continuous pixels and the whole image, this paper will extend the process object to the contour of the image, and improves the detail processing from the existing style transfer examples. From the contour of images, the target image retains the contour feature of style image and the content of original image, in other word, gives the contour style of style image to original image. Finally, the style transfer effect based on the original image contour is obtained with some defects. The work can be easily extended to the aspects of video and 3D images.

AB - The aim of style transfer is giving the style from one picture to another. The application of neural network in image processing separates the high level features and low level features of the image in the process of style transfer, and derives a variety of methods and optimization for style processing. The style transfer generates new images by separating and recombining the content and style of original images. In this process, various factors such as color and illumination will affect the result. The traditional algorithm only focuses on continuous pixels and the whole image, this paper will extend the process object to the contour of the image, and improves the detail processing from the existing style transfer examples. From the contour of images, the target image retains the contour feature of style image and the content of original image, in other word, gives the contour style of style image to original image. Finally, the style transfer effect based on the original image contour is obtained with some defects. The work can be easily extended to the aspects of video and 3D images.

KW - contour processing

KW - convolutional neural network

KW - deep learning

KW - style transfer

UR - http://www.scopus.com/inward/record.url?scp=85063877037&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/contour-image-styletransferbased-convolutional-neural-network

U2 - 10.1117/12.2521489

DO - 10.1117/12.2521489

M3 - Conference contribution

AN - SCOPUS:85063877037

T3 - Proceedings of SPIE

BT - International Workshop on Advanced Image Technology, IWAIT 2019

A2 - Kemao, Q

A2 - Hayase, K

A2 - Lau, PY

A2 - Lie, WN

A2 - Lee, YL

A2 - Srisuk, S

A2 - Yu, L

PB - SPIE

T2 - International Workshop on Advanced Image Technology 2019, IWAIT 2019

Y2 - 6 January 2019 through 9 January 2019

ER -

ID: 44016804