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We were a priori interested in posterior parietal cortex (PPC) that is involved in top-down attentional selection of targets in noise [ 15] by means of figure-ground segmentation [ 16] and learning [ 17], in contrast to ventral areas that process disparity-defined forms [ 18, 19] and feature templates [ 20].

Fifth row: the post processed disparity maps.

Here, we deploy the same features to model a perceptually processed disparity distribution.

The post processed disparity maps are presented in the fifth row.

As shown in the last row of the Fig. 2, post processed disparity maps were manually enhanced using the (1) gradient information to estimate the edges and (2) neighboring disparity values.

These HDR disparity maps are shown as post processed disparity maps in Fig. 2. begin{aligned} C_{i,d} = {alpha} {cdot} text{min}left[||R_{i} - R'_{i-d}||, {tau}_{1}right] + (1-{alpha}) {cdot} text{min}left[||{bigtriangledown}_{x}R_{i} - {bigtriangledown}_{x}R'_{i-d}||, {tau}_{2}right].

Because sRNAs regulate myriad metabolic processes, this disparity in sRNA repertoires between closely related bacteria might also contribute to niche adaptation and speciation events.

In other words, the post processed HDR disparities can be used as the ground truth.

It does not need threshold values and a calculation process to determine disparity estimation skipping.

The overall process of the disparity estimation skipping algorithm based on the motion activity is shown in Figure 3.

The peak broadening and peak position depend on the characteristics of the particles involved in the optical process, and the disparity in the peak position can be attributed to the same [42].

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