scholarly journals Wavelets and Optical Flow Motion Estimation

2013 ◽  
Vol 6 (1) ◽  
pp. 116-137 ◽  
Author(s):  
P. Dérian
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Flow Motion

scholarly journals Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in the Drosophila oocyte

2020 ◽  
Vol 31 (12) ◽  
pp. 1246-1258 ◽  
Author(s):  
Maik Drechsler ◽  
Lukas F. Lang ◽  
Layla Al-Khatib ◽  
Hendrik Dirks ◽  
Martin Burger ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Cytoplasmic Streaming ◽  
Flow Analysis ◽  
Flow Motion ◽  
A Cell

Here we introduce an optical flow motion estimation approach to study microtubule (MT) orientation in the Drosophila oocyte, a cell displaying substantial cytoplasmic streaming. We show that MT polarity is affected by the regime of these flows and, furthermore, that the presence of flows is necessary for MTs to adopt their proper polarity.

A robust optical flow motion estimation and correction method for IRT imaging in brain surgery

2020 ◽  
pp. 1-26
Author(s):  
Yahya Moshaei-Nezhad ◽  
Juliane Müller ◽  
Christian Schnabel ◽  
Matthias Kirsch ◽  
Ronald Tetzlaff
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Correction Method ◽  
Brain Surgery ◽  
Flow Motion ◽  
Estimation And Correction

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.

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