Zobrazeno 1 - 10
of 376
pro vyhledávání: '"Neural Style Transfer"'
Publikováno v:
Discover Artificial Intelligence, Vol 4, Iss 1, Pp 1-15 (2024)
Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer visi
Externí odkaz:
https://doaj.org/article/00835939ebef4be8b65f917e0086fd99
Publikováno v:
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), Vol 10, Iss 2, Pp 379 – 390-379 – 390 (2024)
Penelitian ini dilakukan dengan mengaplikasikan metode Neural Style Transfer pada berbagai set gambar konten dan gaya, kemudian menghitung nilai FSIM untuk setiap pasangan gambar hasil dan gambar asli. Analisa dilakukan terhadap faktor-faktor seperti
Externí odkaz:
https://doaj.org/article/ceb8e9e8f2334bca9cb214a679a1c80d
Autor:
Sunder Ali Khowaja, Sultan Almakdi, Muhammad Ali Memon, Parus Khuwaja, Adel Sulaiman, Ali Alqahtani, Asadullah Shaikh, Abdullah Alghamdi
Publikováno v:
Heliyon, Vol 10, Iss 5, Pp e27012- (2024)
The field of neural style transfer refers to the re-rendering of content image while fusing the features of a style image. The recent studies either focus on multiple style transfer or arbitrary style transfer while using perceptual and fixpoint cont
Externí odkaz:
https://doaj.org/article/3fc3acd9d08444629e1a233fb7a3517d
Publikováno v:
Human-Centric Intelligent Systems, Vol 3, Iss 4, Pp 487-507 (2023)
Abstract Semantic image synthesis plays an important role in the development of Advanced Driver Assistance System (ADAS). Street objects detection might be erroneous during raining or when images from vehicle’s camera are blurred, which can cause s
Externí odkaz:
https://doaj.org/article/43bf7faa22c4470c99709e5126970009
Synthetic Data-Driven Real-Time Detection Transformer Object Detection in Raining Weather Conditions
Autor:
Chen-Yu Hao, Yao-Chung Chen, Tai-Tien Chen, Ting-Hsuan Lai, Tien-Yin Chou, Fang-Shii Ning, Mei-Hsin Chen
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4910 (2024)
Images captured in rainy weather conditions often suffer from contamination, resulting in blurred or obscured objects, which can significantly impact detection performance due to the loss of identifiable texture and color information. Moreover, the q
Externí odkaz:
https://doaj.org/article/1be6bd7104854450840c2e10095d8253
Autor:
Jeong-Sik Lee, Hyun-Chul Choi
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 10, Pp 101850- (2023)
Early feed-forward neural methods of arbitrary image style transfer utilized the encoded feature map up to its second-order statistics, i.e., mean and variance (or covariance) of the encoded feature map. Recent methods have begun to utilize feature s
Externí odkaz:
https://doaj.org/article/a11e17d9a4b94b36a64ccf0ea46f4d9a
Publikováno v:
IEEE Access, Vol 11, Pp 101443-101459 (2023)
The significant increase in drug abuse cases prompts developers to investigate techniques that mimic the hallucinations imagined by addicts and abusers, in addition to the increasing demand for the use of decorative images resulting from the use of c
Externí odkaz:
https://doaj.org/article/43311235892645a78a34eb3bb5328e17
Autor:
Chin-Chen Chang, Ping-Hao Peng
Publikováno v:
Journal of Imaging, Vol 10, Iss 2, p 44 (2024)
Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation bas
Externí odkaz:
https://doaj.org/article/57ccc7f400574c47963b4bbc5853ebae
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