An Efficient Motion Estimation Method for H.264-Based Video Transcoding with Spatial Resolution Conversion

2007 ◽  
Author(s):  
Jiao Wang ◽  
En-hui Yang ◽  
Xiang Yu
Keyword(s):  
Motion Estimation ◽  
Spatial Resolution ◽  
Estimation Method ◽  
Video Transcoding

Visual Odometry Based on 3D-3D and 3D-2D Motion Estimation Method

2018 ◽  
Author(s):  
Kai Cao ◽  
Xuemeng Yang ◽  
Song Gao ◽  
Chaobo Chen ◽  
Jiaoru Huang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Estimation Method ◽  
Visual Odometry

scholarly journals Determination of Lateral Modulation Apodization Functions Using a Regularized, Weighted Least Squares Estimation

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Chikayoshi Sumi
Keyword(s):  
Least Squares ◽  
Spatial Resolution ◽  
Soft Tissues ◽  
Displacement Vector ◽  
Weighted Least Squares ◽  
Strain Tensor ◽  
Estimation Method ◽  
Modulation Method ◽  
Value Decomposition

Recently, work in this group has focused on the lateral cosine modulation method (LCM) which can be used for next-generation ultrasound (US) echo imaging and tissue displacement vector/strain tensor measurements (blood, soft tissues, etc.). For instance, in US echo imaging, a high lateral spatial resolution as well as a high axial spatial resolution can be obtained, and in tissue displacement vector measurements, accurate measurements of lateral tissue displacements as well as of axial tissue displacements can be realized. For an optimal determination of an apodization function for the LCM method, the regularized, weighted minimum-norm least squares (WMNLSs) estimation method is presented in this study. For designed Gaussian-type point spread functions (PSFs) with lateral modulation as an example, the regularized WMNLS estimation in simulations yields better approximations of the designed PSFs having wider lateral bandwidths than a Fraunhofer approximation and a singular-value decomposition (SVD). The usefulness of the regularized WMNLS estimation for the determination of apodization functions is demonstrated.

scholarly journals Glacier Surface Motion Estimation from SAR Intensity Images Based on Subpixel Gradient Correlation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4396
Author(s):  
Li Fang ◽  
Zhen Ye ◽  
Shu Su ◽  
Jian Kang ◽  
Xiaohua Tong
Keyword(s):  
Motion Estimation ◽  
Estimation Method ◽  
Correlation Method ◽  
Image Correlation ◽  
Matching Method ◽  
Glacier Surface ◽  
Surface Motion ◽  
Alpine Regions ◽  
Intensity Images ◽  
Comparative Results

With the current extensive availability of synthetic-aperture radar (SAR) datasets with high temporal (e.g., a repeat cycle of a few or a dozen days) and spatial resolution (e.g., in the order of ∼1 m), radar remote sensing possesses an increasing potential for the monitoring of glacier surface motion thanks to the nearly weather and time-independent advantages. This paper proposes a robust subpixel frequency-based image correlation method for dense matching and integrates the improved matching into a workflow of glacier surface motion estimation using SAR intensity images with specific pre-processing and post-processing steps. The proposed matching method combines complex edge maps and local upsampling in the frequency domain for subpixel intensity tracking, which ensure the accuracy and robustness of glacier surface motion estimation. Experiments were carried out with TerraSAR-X and Sentinel-1 images covering two glacier areas in pole and alpine regions. The results of the monitoring and investigation of glacier motion validate the feasibility and reliability of the presented motion estimation method based on subpixel gradient correlation. The comparative results using both simulated and real SAR data indicate that the proposed matching method outperforms commonly used correlation-based matching methods in terms of matching accuracy and the ability to obtain correct matches.

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