scholarly journals A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics

2019 ◽  
Vol 127 (10) ◽  
pp. 1413-1425
Author(s):  
Mohammad Tavakolian ◽  
Abdenour Hadid
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pain Intensity ◽  
Intensity Estimation

scholarly journals SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 551
Author(s):  
Chih-Wei Lin ◽  
Xiuping Huang ◽  
Mengxiang Lin ◽  
Sidi Hong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Weather Conditions ◽  
Critical Issue ◽  
Activation Function ◽  
Precipitation Intensity ◽  
The Self ◽  
Intensity Estimation ◽  
Signal Filtering ◽  
Complex Models

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network

2018 ◽  
Vol 27 (2) ◽  
pp. 692-702 ◽  
Author(s):  
Ritesh Pradhan ◽  
Ramazan S. Aygun ◽  
Manil Maskey ◽  
Rahul Ramachandran ◽  
Daniel J. Cecil
Keyword(s):  
Neural Network ◽  
Tropical Cyclone ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Tropical Cyclone Intensity ◽  
Intensity Estimation ◽  
Cyclone Intensity
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