MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography

2021 ◽  
Author(s):  
Ali Kafaei Zad Tehrani ◽  
Hassan Rivaz
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Ultrasound Elastography

scholarly journals Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jie Shen ◽  
Mengxi Xu ◽  
Xinyu Du ◽  
Yunbo Xiong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Video Surveillance ◽  
Reconstruction Method ◽  
Motion Information ◽  
Learning Networks ◽  
Urban Computing ◽  
Deep Convolution Network

Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient.

scholarly journals An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

2019 ◽  
Vol 9 (14) ◽  
pp. 2808 ◽  
Author(s):  
Yahui Peng ◽  
Xiaochen Liu ◽  
Chong Shen ◽  
Haoqian Huang ◽  
Donghua Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Navigation System ◽  
Moving Objects ◽  
Harmful Effect ◽  
Improved Method ◽  
Vision Navigation ◽  
Vehicle Velocity ◽  
Flow Algorithm

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

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