Zobrazeno 1 - 10
of 6 832
pro vyhledávání: '"neural nets"'
Autor:
QiaoSu Wang, Qiaomei Ma
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4297-4309 (2024)
Abstract Conventional computed tomography (CT) images often suffer from blurred edges and unclear details. Image super‐resolution methods can significantly enhance CT image quality, thereby improving diagnostic accuracy. To better extract detailed
Externí odkaz:
https://doaj.org/article/f1d25886b0764b178530ef15af44e063
Publikováno v:
International Journal of Intelligent Computing and Cybernetics, 2024, Vol. 17, Issue 4, pp. 759-782.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJICC-07-2024-0310
Autor:
Marco Baldan, Paolo Di Barba
Publikováno v:
IET Science, Measurement & Technology, Vol 18, Iss 9, Pp 657-666 (2024)
Abstract Physics‐informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the lo
Externí odkaz:
https://doaj.org/article/d0e0cd888b80442eb7dede2982d4cbb1
Autor:
Vijaya Kumar Velpula, Diksha Sharma, Lakhan Dev Sharma, Amarjit Roy, Manas Kamal Bhuyan, Sultan Alfarhood, Mejdl Safran
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 3827-3853 (2024)
Abstract Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it is the leading cause of blindness worldwide. Due to the time‐consuming, inaccurate, and manual nature of traditional methods, automation in glaucoma det
Externí odkaz:
https://doaj.org/article/3af1e87c87d04310b27b36b26254f67b
Autor:
Buntita Pravalpruk, Matthew N. Dailey
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4207-4220 (2024)
Abstract Handwriting is a natural way to communicate and exchange ideas, but converting handwritten diagrams to application‐specific digital formats requires skill and time. Automatic handwritten document conversion can save time, but diagrams and
Externí odkaz:
https://doaj.org/article/3bee6b6625984edc81005f44eb50448f
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 3801-3814 (2024)
Abstract Image hiding is a task that embeds secret images in digital images without being detected. The performance of image hiding has been greatly improved by using the invertible neural network. However, current image hiding methods are less robus
Externí odkaz:
https://doaj.org/article/4ec66acbb0164eef9f422b479c886abf
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4310-4327 (2024)
Abstract Image denoising is one of the fundamental problems in image processing. Convolutional neural network (CNN) based denoising approaches have achieved better performance than traditional methods, such as STROLLR and BM3D. However, CNNs can easi
Externí odkaz:
https://doaj.org/article/5cd393aea3a84aba83d7a5941e0a738e
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4066-4080 (2024)
Abstract License plate recognition is crucial in Intelligent Transportation Systems (ITS) for vehicle management, traffic monitoring, and security inspection. In highway scenarios, this task faces challenges such as diversity, blurriness, occlusion,
Externí odkaz:
https://doaj.org/article/a9c60dfa7824490a9895f0bb3b73a512
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4422-4431 (2024)
Abstract Detecting taxi passengers is crucial for assessing taxi driver behavior, which plays a significant role in regulating the taxi industry. Despite the advancements in deep learning, object detection algorithms have not been extensively applied
Externí odkaz:
https://doaj.org/article/3aaee50136ca4c4cb9fb9bf6d5b8e32c
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 11, Pp 2059-2077 (2024)
Abstract Practical applications of graph neural networks (GNNs) in transportation are still a niche field. There exists a significant overlap between the potential of GNNs and the issues in strategic transport modelling. However, it is not clear whet
Externí odkaz:
https://doaj.org/article/613da8c5503842b893d8d86ec5cb17b9