Three-dimensional motion estimation by straight-line and endpoint optical flow

2014 ◽  
Author(s):  
Congxuan Zhang ◽  
Zhen Chen ◽  
Ming Li ◽  
Shuigen Wei
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Three Dimensional ◽  
Straight Line

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.

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

scholarly journals Three-Dimensional (3-D) Immersed-Boundary (IB) Based Tornado Model for Computational Analysis of Disastrous Wind Load on Buildings

2021 ◽  
Author(s):  
Xixiong Guo ◽  
Jun Cao
Keyword(s):  
Wind Field ◽  
High Speed ◽  
Outer Boundary ◽  
Three Dimensional ◽  
Influence Factors ◽  
Research Direction ◽  
Wind Loading ◽  
Immersed Boundary ◽  
Design Strategies ◽  
Straight Line

This study is aimed at developing a novel computational framework that can essentially simulate a tornadic wind field and investigate the wind loadings on ground constructions. It is well known that tornado is a highly turbulent airflow that simultaneously translates, rotates and updrafts with a high speed. Tornadoes induce a significantly elevated level of wind forces if compared to a straight-line wind. A suitably designed building for a straight-line wind would fail to survive when exposed to a tornadic-like wind of the same wind speed. It is necessary to design buildings that are more resistant to tornadoes. Since the study of tornado dynamics relying on field observations and laboratory experiments is usually expensive, restrictive, and time-consuming, computer simulation mainly via the large eddy simulation (LES) method has become a more attractive research direction in shedding light on the intricate characteristics of a tornadic wind field. For numerical simulation of a tornado-building interaction scenario, it looks quite challenging to seek a set of physically-rational and meanwhile computationally-practical boundary conditions to accompany traditional CFD approaches; however, little literature can be found, as of today, in three-dimensional (3D) computational tornado dynamics study. Inspired by the development of the immersed boundary (IB) method, this study employed a re-tailored Rankine-combined vortex model (RCVM) that applies the “relative motion” principle to the translational component of tornado, such that the building is viewed as “virtually” translating towards a “pinned” rotational flow that remains time-invariant at the far field region. This revision renders a steady-state kinematic condition applicable to the outer boundary of a large tornado simulation domain, successfully circumventing the boundary condition updating process that the original RCVM would have to suffer, and tremendously accelerating the computation. Wind loading and its influence factors are comprehensively investigated and analyzed both on a single building and on a multiple-building configuration. The relation between the wind loadings and the height and shape of the building is also examined in detail. Knowledge of these loadings may lead to design strategies that can enable ground construction to be more resistant to tornadoes, reducing the losses caused by this type of disastrous weather.

scholarly journals On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Jeyarajan Thiyagalingam ◽  
Daniel Goodman ◽  
Julia A. Schnabel ◽  
Anne Trefethen ◽  
Vicente Grau
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Three Dimensional ◽  
Basic Research ◽  
Estimation Algorithm ◽  
Image Sequences ◽  
Multicore Architectures ◽  
Gpu Architectures ◽  
Access Patterns ◽  
Performance Gains

Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results.

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