Robust Hand Pose Regression Using Convolutional Neural Networks

2017 ◽  
pp. 591-602
Author(s):  
Francisco Gomez-Donoso ◽  
Sergio Orts-Escolano ◽  
Miguel Cazorla
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Pose

scholarly journals Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

2019 ◽  
Vol 41 (4) ◽  
pp. 956-970 ◽  
Author(s):  
Liuhao Ge ◽  
Hui Liang ◽  
Junsong Yuan ◽  
Daniel Thalmann
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

Graph Convolutional Neural Networks-based 3D Hand Pose Estimation over Point Clouds

2021 ◽  
Author(s):  
John Alejandro Castro-Vargas ◽  
Pablo Martinez-Gonzalez ◽  
Sergiu Oprea ◽  
Alberto Garcia-Garcia ◽  
Sergio Orts-Escolano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Point Clouds ◽  
Hand Pose Estimation ◽  
Hand Pose

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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