Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

2019

Series

PDF

Computer Engineering

Convolutional neural network

Articles 1 - 4 of 4

Full-Text Articles in Engineering

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu May 2019

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available ...


Solid Spherical Energy (Sse) Cnns For Efficient 3d Medical Image Analysis, Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet, Adrien Depeursinge Jan 2019

Solid Spherical Energy (Sse) Cnns For Efficient 3d Medical Image Analysis, Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet, Adrien Depeursinge

Session 2: Deep Learning for Computer Vision

Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of ...