S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 33, Number 9(4) (2021)
Copyright(C) MYU K.K.
pp. 3325-3332
S&M2692 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3402
Published: September 30, 2021

Optimized and Improved Methods of Image Style Transfer for Local Reinforcement [PDF]

Yong Li, Yan Wang, Hsien-Wei Tseng, Hongkun Huang, and Chun-Chi Chen

(Received March 26, 2021; Accepted September 2, 2021)

Keywords: image style transfer, deep learning, image segmentation, DeepLab2

Image style transfer, which commonly refers to adding a designated image style to a target content image, is now widely used in the movie industry, animation design, and game rendering, providing strong visual effects and cultural influences. However, there is no common criterion for evaluating the performance of image style transfer. In addition, people are more interested in local regions of images. This paper provides some revised methods to meet customer demand, focusing on an optimized image segmentation method based on DeepLab2, a semantic segmentation method, and fully connected conditional random fields (FCCRFs) for local image style transfer, with experiments demonstrating their usefulness and efficiency.

Corresponding author: Hsien-Wei Tseng, Chun-Chi Chen


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yong Li, Yan Wang, Hsien-Wei Tseng, Hongkun Huang, and Chun-Chi Chen, Optimized and Improved Methods of Image Style Transfer for Local Reinforcement, Sens. Mater., Vol. 33, No. 9, 2021, p. 3325-3332.



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