Kalman Filter Based Error Resilience for H.264 Motion Vector Recovery

2005 ◽  
pp. 664-674
Author(s):  
Ki-Hong Ko ◽  
Seong-Whan Kim
Keyword(s):  
Kalman Filter ◽  
Motion Vector ◽  
Error Resilience

Motion Vector Re-estimation for Trans-coding Using Kalman Filter

2007 ◽  
Author(s):  
Chih-Shan Liu ◽  
Jen-Chun Lin ◽  
Nai-Chung Yang ◽  
Chung-Ming Kuo
Keyword(s):  
Kalman Filter ◽  
Motion Vector

scholarly journals Sensitivity of the Track and Intensity Forecasts of Typhoon Megi (2010) to Satellite-Derived Atmospheric Motion Vectors with the Ensemble Kalman Filter

2012 ◽  
Vol 29 (12) ◽  
pp. 1794-1810 ◽  
Author(s):  
Chanh Q. Kieu ◽  
Nguyen Minh Truong ◽  
Hoang Thi Mai ◽  
Thanh Ngo-Duc
Keyword(s):  
Kalman Filter ◽  
Motion Vector ◽  
The Philippines ◽  
Intensity Change ◽  
Track Forecast ◽  
Recent Effort ◽  
Low Level ◽  
Sharp Turn ◽  
Atmospheric Motion ◽  
Upper Level

Abstract In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development. Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills.

scholarly journals Kalman filter based Motion Vector Recovery for H.264

2007 ◽  
Vol 14D (7) ◽  
pp. 801-808
Author(s):  
Ki-Hong Ko ◽  
Seong-Whan Kim
Keyword(s):  
Kalman Filter ◽  
Motion Vector

Selective motion vector redundancies for improved error resilience in HEVC

2014 ◽  
Author(s):  
J. Carreira ◽  
E. Ekmekcioglu ◽  
A. Kondoz ◽  
P. Assuncao ◽  
S. Faria ◽  
...  
Keyword(s):  
Motion Vector ◽  
Error Resilience

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

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