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Motion Estimation

Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.

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

Papers

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Showing 301–325 of 741 papers

TitleStatusHype
Instantaneous Perception of Moving Objects in 3D—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
Learned Video Compression—0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression—0
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor—0
Deep Parametric Continuous Convolutional Neural Networks—0
Improving HEVC Encoding of Rendered Video Data Using True Motion Information—0
Attacking Motion Estimation with Adversarial Snow—0
Agent Prioritization for Autonomous Navigation—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
Improvements of Motion Estimation and Coding using Neural Networks—0
Improved Motion Plane Adaptive 360-Degree Video Compression Using Affine Motion Models—0
Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection—0
AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression—0
IM-Net for High Resolution Video Frame Interpolation—0
Image-Space Gridding for Nonrigid Motion-Corrected MR Image Reconstruction—0
Intrinsic Temporal Regularization for High-resolution Human Video Synthesis—0
DeepLO: Geometry-Aware Deep LiDAR Odometry—0
Image Quality Assessment for Rigid Motion Compensation—0
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods—0
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