The contour image style-transfer-based convolutional neural network

Nan Deng; Jing Li; Xingce Wang; Zhongke Wu; Yan Fu; Wuyang Shui; Mingquan Zhou; Vladimir Korkhov; Luciano Paschoal Gaspary

doi:10.1117/12.2521489

22 March 2019 The contour image style-transfer-based convolutional neural network

Nan Deng, Jing Li, Xingce Wang, Zhongke Wu, Yan Fu, Wuyang Shui, Mingquan Zhou, Vladimir Korkhov, Luciano Paschoal Gaspary

Author Affiliations +

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110493C (2019) https://doi.org/10.1117/12.2521489
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

Citation Download Citation

Nan Deng, Jing Li, Xingce Wang, Zhongke Wu, Yan Fu, Wuyang Shui, Mingquan Zhou, Vladimir Korkhov, and Luciano Paschoal Gaspary "The contour image style-transfer-based convolutional neural network", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493C (22 March 2019); https://doi.org/10.1117/12.2521489

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available