scholarly journals MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Wenbo Bao ◽  
Wei-Sheng Lai ◽  
Xiaoyun Zhang ◽  
Zhiyong Gao ◽  
Ming-Hsuan Yang
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Motion Compensation

scholarly journals Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 222
Author(s):  
Baigan Zhao ◽  
Yingping Huang ◽  
Hongjian Wei ◽  
Xing Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Estimation ◽  
Optical Flow ◽  
Feature Representation ◽  
Sequential Learning ◽  
Navigation Systems ◽  
Motion State ◽  
Training Schemes ◽  
Sequential Images

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches.

Cardiac motion estimation using pyramid, warping, and cost volume neural network

2021 ◽  
Author(s):  
Catharine V. Graves ◽  
Ramon A. Moreno ◽  
Marina F. S. Rebelo ◽  
Adriano Bordignom ◽  
Cesar H. Nomura ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Cardiac Motion

Analysis and Implementation of MPEG-4 Motion Codec

2012 ◽  
Vol 268-270 ◽  
pp. 1667-1670
Author(s):  
Yun Peng Liu ◽  
Ren Fang Wang ◽  
Jin Li
Keyword(s):  
Motion Estimation ◽  
Motion Compensation ◽  
Block Matching ◽  
Modified Block

Motion codec is the key technique of MPEG-4 VM. The motion encoding and decoding use “Unrestricted Motion Estimation”, “Advanced Prediction”, “Overlapped Motion Compensation”, in addition, the particular “Repetitive Padding”, and “Modified Block Matching “ are added for MPEG-4. At the same time, some questions during motion are explained carefully, and some new viewpoints are brought forward.

scholarly journals Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

2019 ◽  
Vol 82 (4) ◽  
pp. 1452-1461 ◽  
Author(s):  
Melissa W. Haskell ◽  
Stephen F. Cauley ◽  
Berkin Bilgic ◽  
Julian Hossbach ◽  
Daniel N. Splitthoff ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Convolutional Neural Network ◽  
Motion Correction ◽  
Motion Model ◽  
Accelerated Motion
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