Results 241 to 250 of about 3,878,820 (309)

Motion estimation with integrated motion models

Proceedings of the 42nd annual Southeast regional conference, 2004
Conventional motion estimation algorithms rely on motion vectors characterizing translations and thus have limitations in capturing transformations of objects in video scenes such as scaling, rotations and deformations. In this paper, we introduce integrated motion models based on the Lie derivatives to improve the motion estimation accuracy.
Mahesh Nalasani, W. David Pan, Seong-Moo Yoo   +2 more
openaire   +1 more source

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation ...
Chen Qin, Wenjia Bai, Jo Schlemper, Steffen Erhard Petersen, S. Piechnik, S. Neubauer, D. Rueckert   +6 more
semanticscholar   +1 more source

Motion estimation on interlaced video

SPIE Proceedings, 2005
Motion compensated de-interlacing and motion estimation based on Yen's generalisation of the sampling theorem (GST) have been proposed by Delogne and Vandendorpe. Motion estimation methods using three-fields have been designed on a block-by-block basis, minimising the difference between two GST predictions.
Calina Ciuhu, Gerard de Haan
openaire   +1 more source

Motion Guided 3D Pose Estimation from Videos

European Conference on Computer Vision, 2020
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced ...
Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin   +3 more
semanticscholar   +1 more source

Optimal motion and structure estimation

Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1993
The problem of estimating motion and structure of a rigid scene from two perspective monocular views is studied. The optimization approach presented is motivated by the following observations of linear algorithms: (1) for certain types of motion, even pixel-level perturbations (such as digitization noise) may override the information characterized by ...
Juyang Weng, Narendra Ahuja, Thomas S. Huang   +2 more
openaire   +1 more source

A Fast Motion Estimation Using Prediction of Motion Estimation Error

2004
This paper presents a modified MSEA (multi-level successive elimination algorithm) which gives less computational complexity. We predict a motion estimation error using the norms at the already processed levels in the MSEA scheme and then decide on if the following levels should be proceeded using the predicted result.
Hyun Soo Kang 0001, Seong-Mo Park, Si-Woong Lee, Jae-Gark Choi, Byoung-Ju Yun   +4 more
openaire   +1 more source

An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant-Based Search Algorithm in HEVC

Circuits, systems, and signal processing, 2021
F. H. Shajin, P. Rajesh, M. Raja
semanticscholar   +1 more source

3D ego-Motion Estimation Using low-Cost mmWave Radars via Radar Velocity Factor for Pose-Graph SLAM

IEEE Robotics and Automation Letters, 2021
Yeong-Sang Park, Young-sik Shin, Joowan Kim, Ayoung Kim   +3 more
semanticscholar   +1 more source

MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos

Computer Vision and Pattern Recognition
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes.
Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely   +8 more
semanticscholar   +1 more source

Dense motion estimation of particle images via a convolutional neural network

Experiments in Fluids, 2019
Shengze Cai, Shichao Zhou, Chao Xu, Qi Gao   +3 more
semanticscholar   +1 more source