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It appears as if they were trying to identify the person's intentions or expectations, by monitoring his visual attention, for the eyes carry a great deal of information on one's attentional state [16].
Most leading computational models of visual attention (for example, [23], [37], [38]) and neglect [34], [35], [36] tend to use covert rather than overt attentional movements so that the entire scene is processed for each location that is attended.
It reflects the effect of the viewing procedure on visual attention for more or less conspicuous lesions [ 21].
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Therefore, this paper proposes a new visual attention algorithm for target detection.
Other information, such as motion and depth, shall be utilized to improve visual attention model for dynamic stereoscopic visual scenes.
Fang et al. [21] proposed a visual attention model for stereoscopic images based on the contrast between low-level features.
The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses.
The visual attention model for synthetic aperture radar (SAR) image target detection plays a positive role because the human visual system focuses on the areas of interest and rapidly decides on them [1].
Kim et al. [15] designed a stereoscopic visual attention algorithm for 3D video based on multiple perceptual stimuli, which assumes that pixels closer to observers and at the front of the screen are more salient.
A follow-up study examining Bimanual Marking Menu suggests that the performance of two-handed techniques may be reduced due to a split in visual attention required for certain techniques.
The purpose of this paper is to propose an avian eye-inspired visual attention approach for unmanned air vehicles (UAV), with the objective of improving the capability of UAV target detection.
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