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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 33, Number 9(4) (2021)
Copyright(C) MYU K.K.
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  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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- Deep Learning Prediction Model of Mortality Including Brain-type Natriuretic Peptide in Patients with Acute Decompensated Heart Failure
Hirotaka Takizawa, Masatoshi Minamisawa, Hirohiko Motoki, Koichiro Kuwahara, and Masaki Yamaguchi
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