Results 21 to 30 of about 6,781,577 (302)

Hyperspectral image classification of wolfberry with different geographical origins based on three-dimensional convolutional neural network

International Journal of Food Properties, 2021
The hyperspectral image is a three-dimensional (3D) hypercube with spectral and spatial continuity. Traditional hyperspectral imaging (HSI) processing mainly focuses on spectral information.
Qingshuang Mu, Zhilong Kang, Yanju Guo, Lei Chen, Shenyi Wang, Yuchen Zhao   +5 more
doaj   +1 more source

3D convolutional neural networks for detecting intracranial aneurysms on brachiocephalic arteries CTA scans

Инновационная медицина Кубани, 2023
Background: Computed tomography angiography (CTA) is the primary and minimally invasive imaging modality currently used for diagnosis and monitoring of intracranial aneurysms as well as preoperative planning of their treatment.
E. I. Zyablova, S. G. Sinitsa, I. A. Zayats, A. A. Khalafyan, D. O. Kardailskaya, V. A. Porhanov   +5 more
doaj   +1 more source

Asymmetric 3D Convolutional Neural Networks for action recognition [PDF]

Pattern Recognition, 2019
Convolutional Neural Network based action recognition methods have achieved significant improvements in recent years. The 3D convolution extends the 2D convolution from operating on one single frame to a video clip, so it is able to extract effective spatial-temporal features for better analysis of human activities in videos.
Hao Yang 0010, Chunfeng Yuan, Bing Li 0001, Yang Du, Junliang Xing, Weiming Hu 0004, Stephen J. Maybank   +6 more
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3D Convolutional Neural Networks for Human Action Recognition [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs.
Shuiwang Ji, Wei Xu 0007, Ming Yang 0007, Kai Yu 0001   +3 more
openaire   +4 more sources

Sparse 3D convolutional neural networks [PDF]

Procedings of the British Machine Vision Conference 2015, 2015
BMVC ...
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Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

Applied Sciences, 2020
In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment.
Jun Hyeong Jo, Chang-bae Moon
doaj   +1 more source

iOceanSee: A Novel Scheme for Ocean State Estimation Using 3D Mobile Convolutional Neural Network

IEEE Access, 2020
Ocean state estimation is a basic problem in the field of ocean engineering. Under the trend of data-driven, the development of intelligent ship decision-making, ocean energy system design and other aspects, are inseparable from the estimation of wave ...
Huafeng Wu, Yuanyuan Zhang, Jun Wang, Weijun Wang, Jiangfeng Xian, Jing Chen, Xiangyi Zou, Prasant Mohapatra   +7 more
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Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

Energies, 2022
The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural
Sergei Petrov, Tapan Mukerji, Xin Zhang, Xinfei Yan   +3 more
doaj   +1 more source

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

Remote Sensing, 2020
We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area.
Stefan Bachhofner, Ana-Maria Loghin, Johannes Otepka, Norbert Pfeifer, Michael Hornacek, Andrea Siposova, Niklas Schmidinger, Kurt Hornik, Nikolaus Schiller, Olaf Kähler, Ronald Hochreiter   +10 more
doaj   +1 more source

Alzheimer’s Detection through 3D Convolutional Neural Networks

The International FLAIRS Conference Proceedings, 2021
To inform a proper diagnosis and understanding of Alzheimer’s Disease (AD), deep learning has emerged as an alternate approach for detecting physical brain changes within magnetic resonance imaging (MRI). The advancement of deep learning within biomedical imaging, particularly in MRI scans, has proven to be an efficient resource for abnormality ...
Ryan Hogan, Christoforos Christoforou
openaire   +3 more sources