Time-lapse Seismic Receiver Motion Correction Method and Application

2017 ◽  
Author(s):  
DongChuan Xue* ◽  
ZhenYu Zhu
Keyword(s):  
Motion Correction ◽  
Time Lapse ◽  
Correction Method ◽  
Seismic Receiver ◽  
Motion Correction Method

Motion artifacts in endoscopic catheter-based images: simulation and motion correction method

2018 ◽  
Author(s):  
Elham Abouei ◽  
Anthony M. Lee ◽  
Pierre Lane ◽  
Calum MacAulayb ◽  
Stephen Lam ◽  
...  
Keyword(s):  
Motion Correction ◽  
Correction Method ◽  
Motion Artifacts ◽  
Motion Correction Method

Motion Correction With Adaptive Karlman Filter

2011 ◽  
Vol 268-270 ◽  
pp. 1768-1772 ◽  
Author(s):  
Yang Ke Liu ◽  
Chun Zhao Lv ◽  
Chang Li
Keyword(s):  
Kalman Filter ◽  
Digital Image ◽  
Motion Correction ◽  
Correction Method ◽  
Observation Error ◽  
Stabilization System ◽  
Movement Model ◽  
Image Stabilization ◽  
System Noise ◽  
Motion Correction Method

In the digital image stabilization system, Kalman filter is the most commonly used filter for motion correction. When the wanted movements have large assumptions deviation with the movement model, the result of motion correction will cause divergence and even error. For this problem, a novel motion correction method with adaptive Karlman filter is proposed. The back and forth characteristic of the unwanted motion and the smoothness characteristic of the wanted motion is used to adjust the system noise and the observation error adaptively. Experiment results show that the proposed method can effectively distinguish the wanted and the unwanted movement. Compared with the method with fixed parameters, the proposed method takes into account the smoothness and delay of wanted motion at the same time and it is more adaptively.

scholarly journals Improving the robustness of MOLLI T1 maps with a dedicated motion correction algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gaspar Delso ◽  
Laura Farré ◽  
José T. Ortiz-Pérez ◽  
Susanna Prat ◽  
Adelina Doltra ◽  
...  
Keyword(s):  
Standard Method ◽  
Motion Correction ◽  
T1 Mapping ◽  
Heart Diseases ◽  
Rigid Motion ◽  
Qualitative Evaluation ◽  
Correction Method ◽  
Large Database ◽  
Magnetization Relaxation ◽  
Motion Correction Method

AbstractMyocardial tissue T1 constitutes a reliable indicator of several heart diseases related to extracellular changes (e.g. edema, fibrosis) as well as fat, iron and amyloid content. Magnetic resonance (MR) T1-mapping is typically achieved by pixel-wise exponential fitting of a series of inversion or saturation recovery measurements. Good anatomical alignment between these measurements is essential for accurate T1 estimation. Motion correction is recommended to improve alignment. However, in the case of inversion recovery sequences, this correction is compromised by the intrinsic contrast variation between frames. A model-based, non-rigid motion correction method for MOLLI series was implemented and validated on a large database of cardiac clinical cases (n = 186). The method relies on a dedicated similarity metric that accounts for the intensity changes caused by T1 magnetization relaxation. The results were compared to uncorrected series and to the standard motion correction included in the scanner. To automate the quantitative analysis of results, a custom data alignment metric was defined. Qualitative evaluation was performed on a subset of cases to confirm the validity of the new metric. Motion correction caused noticeable (i.e. > 5%) performance degradation in 12% of cases with the standard method, compared to 0.3% with the new dedicated method. The average alignment quality was 85% ± 9% with the default correction and 90% ± 7% with the new method. The results of the qualitative evaluation were found to correlate with the quantitative metric. In conclusion, a dedicated motion correction method for T1 mapping MOLLI series has been evaluated on a large database of clinical cardiac MR cases, confirming its increased robustness with respect to the standard method implemented in the scanner.

scholarly journals Error analysis of motion correction method for laser scanning of moving objects

2014 ◽  
Vol II-5 ◽  
pp. 139-144
Author(s):  
S. Goel ◽  
B. Lohani
Keyword(s):  
Motion Correction ◽  
Laser Scanning ◽  
Moving Objects ◽  
Laser Scanner ◽  
Correction Method ◽  
Object Motion ◽  
Shipping Industry ◽  
Major Application ◽  
Tracking Devices ◽  
Motion Correction Method

The limitation of conventional laser scanning methods is that the objects being scanned should be static. The need of scanning moving objects has resulted in the development of new methods capable of generating correct 3D geometry of moving objects. Limited literature is available showing development of very few methods capable of catering to the problem of object motion during scanning. All the existing methods utilize their own models or sensors. Any studies on error modelling or analysis of any of the motion correction methods are found to be lacking in literature. In this paper, we develop the error budget and present the analysis of one such ‘motion correction’ method. This method assumes availability of position and orientation information of the moving object which in general can be obtained by installing a POS system on board or by use of some tracking devices. It then uses this information along with laser scanner data to apply correction to laser data, thus resulting in correct geometry despite the object being mobile during scanning. The major application of this method lie in the shipping industry to scan ships either moving or parked in the sea and to scan other objects like hot air balloons or aerostats. It is to be noted that the other methods of "motion correction" explained in literature can not be applied to scan the objects mentioned here making the chosen method quite unique. This paper presents some interesting insights in to the functioning of "motion correction" method as well as a detailed account of the behavior and variation of the error due to different sensor components alone and in combination with each other. The analysis can be used to obtain insights in to optimal utilization of available components for achieving the best results.

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