Zobrazeno 1 - 10
of 5 884
pro vyhledávání: '"depth-estimation"'
Publikováno v:
Complex & Intelligent Systems, Vol 11, Iss 1, Pp 1-16 (2024)
Abstract In recent years, deep learning has significantly advanced the development of image depth estimation algorithms. The depth estimation network with single-view input can only extract features from a single 2D image, often neglecting the inform
Externí odkaz:
https://doaj.org/article/ceef544e93cc4e2abc2511f551cd7c90
Publikováno v:
Open Geosciences, Vol 16, Iss 1, Pp 167-86 (2024)
The south-eastern part of the Egyptian desert had been waiting for a long time for an exploration process of hydrocarbon potentiality. In this research, we are addressing the available high resolution airborne magnetic data of Delta Wadi Diit area, w
Externí odkaz:
https://doaj.org/article/0bda39381f7b45ef9138dd6c335e333f
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Monocular depth estimation is an important but challenging task. Although the performance has been improved by adopting various encoder-decoder architectures, the estimated depth maps lack structure details and clear edges due to simple repe
Externí odkaz:
https://doaj.org/article/4649f3d3668d4aad9ec97e84fc311886
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract The existing deep estimation networks often overlook the issue of computational efficiency while pursuing high accuracy. This paper proposes a lightweight self-supervised network that combines convolutional neural networks (CNN) and Transfor
Externí odkaz:
https://doaj.org/article/461376c04c754c8d9d6cb0e65ac4e8cd
Publikováno v:
Acta Electrotechnica et Informatica, Vol 24, Iss 3, Pp 23-27 (2024)
This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including huma
Externí odkaz:
https://doaj.org/article/ae8ba580fdc045f284c8a6504b57ba86
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 7927-7941 (2024)
Abstract Self-supervised monocular depth estimation has always attracted attention because it does not require ground truth data. Designing a lightweight architecture capable of fast inference is crucial for deployment on mobile devices. The current
Externí odkaz:
https://doaj.org/article/0e7d2d375d754ea3b5cecb687d7df956
Publikováno v:
Heritage Science, Vol 12, Iss 1, Pp 1-13 (2024)
Abstract Digitalization of ancient architectures is one of the effective means for the preservation of heritage structures, with 3D reconstruction based on computer vision being a key component of such digitalization techniques. However, Chinese anci
Externí odkaz:
https://doaj.org/article/0903c0c44d454654ac2df3766192b8c4
Autor:
Swaraja Kuraparthi, Teja Sai Vamsi Kanuri, Kiran Mannem, Jamal K, Padmalaya Nayak, Nikolai Ivanovich Vatin, Anil Kumar Saxena, Myasar Mundher Adnan
Publikováno v:
Cogent Engineering, Vol 11, Iss 1 (2024)
In the domain of scene interpretation for autonomous vehicles, it is very crucial to identify and classify the objects in an environment. Deep learning techniques like semantic segmentation, depth estimation, instance segmentation, object detection,
Externí odkaz:
https://doaj.org/article/538da52602bf4267abb74bdb8b912750
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTPassive microwave (PM) remote sensing have been extensively used for snow depth (SD) estimation. However, current SD products from traditional PM data fail to capture the differentiation in mountainous and complex terrains with coarse resolut
Externí odkaz:
https://doaj.org/article/8d96848db44c49ffa054ad6381104ccb
Publikováno v:
Geosystems and Geoenvironment, Vol 3, Iss 4, Pp 100319- (2024)
Prediction of the exact location and depth of underground targets with the very low-frequency electromagnetic (VLF-EM) technique is one of the most important and difficult tasks in geophysical investigations. This study examined and compared the conv
Externí odkaz:
https://doaj.org/article/74b69d33bbe5426098b062bb5796a6df