Results 161 to 170 of about 148,594 (170)

Winograd Algorithm for 3D Convolution Neural Networks

2017
Three-dimensional convolution neural networks (3D CNN) have achieved great success in many computer vision applications, such as video analysis, medical image classification, and human action recognition. However, the efficiency of this model suffers from great computational intensity.
Zelong Wang, Qiang Lan, Hongjun He, Chunyuan Zhang   +3 more
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3D Convolutional Neural Networks for Event-Related Potential detection

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
Deep learning techniques have recently been successful in the classification of brain evoked responses for multiple applications, including brain-machine interface. Single-trial detection in the electroencephalogram (EEG) of brain evoked responses, like event-related potentials (ERPs), requires multiple processing stages, in the spatial and temporal ...
H, Cecotti, G, Jha
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Pre-Impact Fall Detection Using 3D Convolutional Neural Network

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 2019
Early fall detection is an important issue during gait rehabilitation training. This paper proposes an approach for pre-impact fall detection during gait rehabilitation training based on a 3D convolutional neural network (CNN). Firstly, pre-training data is collected and used to pre-train the 3D CNN to differentiate between a normal walking and a fall ...
Shengchao, Li, Hao, Xiong, Xiumin, Diao
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Video Steganography Using 3D Convolutional Neural Networks

2019
In an steganography we intend to hide information of interest in another data, aiming at secure data transaction, such as hiding an image in another image. The same task could be performed in video steganography. One approach to steganography is through manipulating least significant bits (LSB). Increasing the precision of the approaches in this regard,
Mahdi Abdolmohammadi, Rahil Mahdian Toroghi, Azam Bastanfard   +2 more
openaire   +1 more source

3D-GCNN - 3D Object Classification Using 3D Grid Convolutional Neural Networks

2019
In this paper we propose to solve the problem of 3D Object Classification on point cloud data. We propose a 3D CNN architecture which we call 3D-GCNN that consumes point cloud data directly and performs classification. We present a novel method to represent the point cloud data using a margin based density occupancy grids which creates a minimum volume
Rishabh Tigadoli, Ramesh Ashok Tabib, Adarsh Jamadandi, Uma Mudenagudi   +3 more
openaire   +1 more source

Violence Detection using 3D Convolutional Neural Networks

2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2022
Jiayi Su, Paris Her, Erik Clemens, Edwin Yaz, Susan Schneider, Henry Medeiros   +5 more
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Permeability Prediction via 3D Convolution Neural Networks

2021
Elmorsy, Mohamed, El-Dakhakhni, Wael, Benzhong Zhao   +2 more
openaire   +1 more source

3D Convolutional Neural Networks for Video Recognition

Communications on Applied Nonlinear Analysis
3D CNNs have proven to be an effective technique for analysing spatiotemporal data particularly in video recognition. By applying convolutions across consecutive video frames, 3D CNNs take into account both spatial and temporal dimensions, in contrast to typical 2D CNNs that process frames one at a time.
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A Review on 3D Convolutional Neural Network

2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 2023
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3D brain image segmentation using 3D tiled convolution neural networks

Pattern Recognition and Tracking XXXV
Md Mahibul Haque, Jobeda Khanam Ria, Fahad Al Mannan, Sadman Majumder, MD Reaz Uddin, Mahjabeen Tamanna Abed, Md. Ashraful Alam   +6 more
openaire   +1 more source