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Figure 12(b) illustrates motion detection, performed at a crossroad under falling snow, with the algorithm.
is the pixel gray level value, with and as, respectively, expressed in (8) and (9). Figure 10 illustrates motion detection performed with the and SBA algorithms.
Figure 7 g) illustrates motion saliency maps and Figure 7(k) shows the ROI extracted on the basis of motion saliency only.
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Illustration of line profile placed on the rat shown in Figure 7, example A. Gated, gating+, and ungated profiles illustrate motion and spatial resolution characteristics.
Figures 3, 4 illustrate motion of discrete plane curves corresponding to M-shock wave solutions (M=1,2, respectively) of the discrete Burgers equation (3.31) with parameters a=π/3, ε=π/4, δ=1, ξ 1=0.
Figures 1 and 2 illustrate motion of plane curves corresponding to M-shock wave solutions (M=1,2, respectively) of the Burgers Eq. (2.9) with (t_{i} = 0 left (i geq 3right)).
This section illustrates the motion detection algorithm (Figure 1).
Although Fig. 1 illustrates horizontal motion, vertical oscillation is also possible because the average decay time of 4 5 min in the normal years corresponds to Brunt Vaisala frequency of vertical oscillation of air parcel in a stable atmosphere.
It is clearly seen from Table 2 that the error norms obtained by the present method are smaller than the other methods [30, 31, 32, 37]. Figure 1 illustrates the motion of solitary wave with c=1, h=0.2, and k=0.025 at different time levels.
The wave generator illustrates wave motion using a series of steel rods joined by a thin torsion wire.
The aims of this study were to investigate the effect of motion artifacts on the retardation values and to illustrate how motion artifacts can be identified.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com