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For the proposed method, the motion saliency is computed based on the assumptions given in Section 3.2.
Moreover, in order to extract STMS descriptor, we introduce a novel trajectory encoding method and based on the proposed encoding method, the motion similarity is computed under Euclidean distance.
To verify that the prototype satisfied the design conditions for realizing the proposed step climbing method, the motion of the mechanism was tested and compared with the simulation results shown in Figure 10.
Using the well-established discrete element method, the motion of vertical particle dampers can be analyzed and classified into three different regions, and the associated damping characteristics can be interpreted.
First, in order to confirm the effectiveness of (i), we show an example result which is obtained by separately performing the motion blur removal and the resolution enhancement in Figure 9. Specifically, after the resolution enhancement based on the proposed SR method, the motion blur removal by Fergus et al. [34] is performed.
We compared our proposed method with the original optical flow method, the motion detection methods based on Kalman filtering [11], background modeling using Gaussian mixture model [12], difference-based spatial temporal entropy image (DSTEI) [13], and forward-backward motion history images (MHI) [14].
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In these methods, the motion is estimated at and around the landmarks vis-à-vis some reference frame.
For motion detection based on the spatio-temporal filter methods, the motion is characterized via the entire three-dimensional (3D) spatio-temporal data volume spanned by the moving person in the image sequence [27 37].
The calculation is not described consistently between the Methods and the legend for Figure 1: in the Methods the motion index is described as a summation, but in the legend it is described as a fraction.
The proposed method models the motion between two key frames and as linear.
Our proposed method revises the motion information based on the characteristics of the 3D visual perception.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com