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The contour image style-transfer-based convolutional neural network
Author(s): Nan Deng; Jing Li; Xingce Wang; Zhongke Wu; Yan Fu; Wuyang Shui; Mingquan Zhou; Vladimir Korkhov; Luciano Paschoal Gaspary
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Paper 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.

Paper Details

Date Published: 22 March 2019
PDF: 11 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493C (22 March 2019); doi: 10.1117/12.2521489
Show Author Affiliations
Nan Deng, Beijing Normal Univ. (China)
Jing Li, Beijing Normal Univ. (China)
Xingce Wang, Beijing Normal Univ. (China)
Zhongke Wu, Beijing Normal Univ. (China)
Yan Fu, Beijing Normal Univ. (China)
Wuyang Shui, Beijing Normal Univ. (China)
Mingquan Zhou, Beijing Normal Univ. (China)
Vladimir Korkhov, St. Petersburg State Univ. (Russian Federation)
Luciano Paschoal Gaspary, Univ. Federal do Rio Grande do Sul (Brazil)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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