scholarly journals Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 149122-149132
Author(s):  
V. Javier Traver ◽  
Roberto Paredes
Keyword(s):  
Neural Networks ◽  
Motion Estimation ◽  
Convolutional Neural Networks ◽  
Parametric Motion

Motion Estimation and Path Planning for Assistive Robotic Devices

2019 ◽  
Author(s):  
Marvin H. Cheng ◽  
Po-Lin Huang ◽  
Hao-Chuan Chu ◽  
E. A. McKenzie
Keyword(s):  
Neural Networks ◽  
Path Planning ◽  
Motion Estimation ◽  
Convolutional Neural Networks ◽  
Daily Activities ◽  
Healthcare Applications ◽  
Initial State ◽  
Predicting Model ◽  
Robotic Devices ◽  
Assistive Robotic

Abstract Assistive robotic devices have recently become a popular tool in various healthcare applications. To better assist users in their daily activities with robotic devices, adequate moving paths of joints need to be adopted based on user’s motions. In this paper, a motion predicting model was proposed. With the model developed using convolutional neural networks (CNNs), the corresponding type of motions can be determined efficiently in the initial state. A deriving procedure of common trajectories of desired motions has also been proposed using the approach of temporal alignment. These derived common trajectories are stored as a library. After the type of a specific motion being identified, paths are then synthesized to drive robotic devices with these derived common trajectories.

scholarly journals A Pilot Study on Convolutional Neural Networks for Motion Estimation From Ultrasound Images

2020 ◽  
Vol 67 (12) ◽  
pp. 2565-2573 ◽  
Author(s):  
Ewan Evain ◽  
Khuram Faraz ◽  
Thomas Grenier ◽  
Damien Garcia ◽  
Mathieu De Craene ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pilot Study ◽  
Motion Estimation ◽  
Convolutional Neural Networks ◽  
Ultrasound Images

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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