Real-time joint estimation of camera orientation and vanishing points

2015 ◽  
Author(s):  
Jeong-Kyun Lee ◽  
Kuk-Jin Yoon
Keyword(s):  
Real Time ◽  
Joint Estimation ◽  
Vanishing Points ◽  
Camera Orientation

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

2017 ◽  
Vol 25 (04) ◽  
pp. 587-603 ◽  
Author(s):  
YUSUKE ASAI ◽  
HIROSHI NISHIURA
Keyword(s):  
Real Time ◽  
Ebola Virus ◽  
Virus Disease ◽  
Critical Role ◽  
Estimation Method ◽  
Case Fatality ◽  
Ascertainment Bias ◽  
Joint Estimation ◽  
Ebola Virus Disease ◽  
Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

scholarly journals Impacts of stochastic models on real-time 3D UAV mapping

2014 ◽  
Vol XL-1 ◽  
pp. 35-44
Author(s):  
M. Alqurashi ◽  
J. Wang
Keyword(s):  
Stochastic Model ◽  
Real Time ◽  
Stochastic Models ◽  
Feature Matching ◽  
Critical Role ◽  
Image Feature ◽  
Mapping Process ◽  
Camera Orientation ◽  
Camera Position

In UAV mapping using direct geo-referencing, the formation of stochastic model generally takes into the account the different types of measurements required to estimate the 3D coordinates of the feature points. Such measurements include image tie point coordinate measurements, camera position measurements and camera orientation measurements. In the commonly used stochastic model, it is commonly assumed that all tie point measurements have the same variance. In fact, these assumptions are not always realistic and thus, can lead to biased 3D feature coordinates. Tie point measurements for different image feature objects may not have the same accuracy due to the facts that the geometric distribution of features, particularly their feature matching conditions are different. More importantly, the accuracies of the geo-referencing measurements should also be considered into the mapping process. In this paper, impacts of typical stochastic models on the UAV mapping are investigated. It has been demonstrated that the quality of the geo-referencing measurements plays a critical role in real-time UAV mapping scenarios.

scholarly journals OBJECT TRACKING CONTROL USING A GIMBAL MECHANISM

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 189-196
Author(s):  
D. J. Regner ◽  
J. D. Salazar ◽  
P. V. Buschinelli ◽  
M. Machado ◽  
D. Oliveira ◽  
...  
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Tracking Control ◽  
Image Plane ◽  
Target Object ◽  
Control Solution ◽  
Detection Model ◽  
Rotation Matrices ◽  
Camera Orientation ◽  
Object Inspection

Abstract. This work describes a control solution for real time object tracking in images acquired for a RPAS on an object inspection environment. This, controlling a 3-axis gimbal mechanism to control a camera orientation embedded to a RPAS, using its image processed for feedback. The objective of control is to maintain the target of interest at the center of the image plane. The proposed solution uses a YOLOv3 object detection model in order to detect the target object and determine, thru rotation matrices, the new desired angles to converge the object’s position to the center of the image. To compare results of the proposed control, a linear control was tuned using a linear PI algorithm. Simulation and practice experiments successfully tracked the desired object in real time using YOLOv3 in both control approaches presented.

scholarly journals On the Temporal Fidelity of Nonlinear Inverse Reconstructions for Real- Time MRI – The Motion Challenge

2014 ◽  
Vol 8 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jens Frahm ◽  
Sebastian Schätz ◽  
Markus Untenberger ◽  
Shuo Zhang ◽  
Dirk Voit ◽  
...  
Keyword(s):  
Real Time ◽  
Median Filter ◽  
Joint Estimation ◽  
Acquisition Time ◽  
Reconstruction Method ◽  
Temporal Filtering ◽  
Inverse Reconstruction ◽  
Object Movement ◽  
T1 Contrast ◽  
Motion Assessment

Purpose: To evaluate the temporal accuracy of a self-consistent nonlinear inverse reconstruction method (NLINV) for real-time MRI using highly undersampled radial gradient-echo sequences and to present an open source framework for the motion assessment of real-time MRI methods. Methods: Serial image reconstructions by NLINV combine a joint estimation of individual frames and corresponding coil sensitivities with temporal regularization to a preceding frame. The temporal fidelity of the method was determined with a phantom consisting of water-filled tubes rotating at defined angular velocity. The conditions tested correspond to real-time cardiac MRI using SSFP contrast at 1.5 T (40 ms resolution) and T1 contrast at 3.0 T (33 ms and 18 ms resolution). In addition, the performance of a post-processing temporal median filter was evaluated. Results: NLINV reconstructions without temporal filtering yield accurate estimations as long as the speed of a small moving object corresponds to a spatial displacement during the acquisition of a single frame which is smaller than the object itself. Faster movements may lead to geometric distortions. For small objects moving at high velocity, a median filter may severely compromise the spatiotemporal accuracy. Conclusion: NLINV reconstructions offer excellent temporal fidelity as long as the image acquisition time is short enough to adequately sample (“freeze”) the object movement. Temporal filtering should be applied with caution. The motion framework emerges as a valuable tool for the evaluation of real-time MRI methods.

REAL-TIME ENSEMBLE-BASED WELL-LOG INTERPRETATION FOR GEOSTEERING

2021 ◽  
Author(s):  
Nazanin Jahani ◽  
◽  
Joaquín Ambía ◽  
Kristian Fossum ◽  
Sergey Alyaev ◽  
...  
Keyword(s):  
Real Time ◽  
Water Saturation ◽  
Joint Estimation ◽  
Well Logs ◽  
Nuclear Density ◽  
Layer By Layer ◽  
Computationally Efficient ◽  
Parameter Uncertainties ◽  
Formation Evaluation ◽  
Look Ahead

The cost of drilling wells on the Norwegian Continen-tal Shelf are extremely high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable computationally efficient and robust work-flows that can interpret well logs and capture uncertain-ties in real time are necessary for successful well place-ment. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Leven-berg Marquardt form of the Ensemble Randomized Max-imum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model param-eters and their uncertainties. In this paper the demon-strate joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallow-sensing nuclear density and shallow to extra-deep EM logs). Numerical experiments performed on a synthetic exam-ple verified that the iterative ensemble-based method can estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of time compared to clas-sical Monte Carlo methods. Extra-deep EM measure-ments are known to provide the best reliable informa-tion for geosteering, and we show that they can be in-terpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow sensing nuclear density logs. Importantly the es-timation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty re-duction of the boundary positions ahead of bit, which is crucial for geosteering decisions and reservoir mapping.

Real time vanishing points detection on smartphones under Manhattan world assumption

2018 ◽  
Vol 115 ◽  
pp. 117-127 ◽  
Author(s):  
Zengshi Huang ◽  
Naijie Gu ◽  
Chuanwen Lin ◽  
Jie Shen ◽  
Jie Chang
Keyword(s):  
Real Time ◽  
Vanishing Points

Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

2014 ◽  
Vol 687-691 ◽  
pp. 270-274 ◽  
Author(s):  
Feng Tian ◽  
Jian Yang Zheng ◽  
Tong Zhang
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Fault Diagnosis ◽  
Real Time ◽  
Flight Control ◽  
Joint Estimation ◽  
Filter Method ◽  
State Variables ◽  
Flight Control System ◽  
Sensor Fault

The fault diagnosis of unmanned aerial vehicle (UAV) flight control system is an important research of UAV in health management. The sensor is the link which easiest to have problems of the flight control system. Making timely and accurate prediction of its faults is particularly important. A strong tracking Kalman Filter method for the sensor fault diagnosis of UAV flight control system was presented in this paper. The parameters of the system were extended to the state variables, the sensor fault observer was constructed, and the joint estimation of states and parameters of flight control system were gotten. The method can be used to real-time estimate the unmeasured states and time-varying parameters. The results of simulation experiments show that the method has a good real-time and accuracy in the sensor fault diagnosis of flight control system.

Sign in / Sign up

Export Citation Format

Share Document