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Our work is different from [20] in the use of saliency models.
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In this section, we discuss the backgrounds of the control of eye movements and the prediction of eye fixations using saliency models.
While we use the saliency model of [22], Luan et al. use the scale-space method of [21].
The results show that the Structure Augmented Monocular Saliency (SAMS) algorithm performs better than against commonly used visual saliency models on the same datasets.
The calibration of the RF model indicated that color indices better discriminated leaves and stem than the sole use of saliency features.
We propose the use of saliency detection for object tracking.
The projected saliency framework enabled to study the interaction of features in the feature activation process of computational saliency models.
In addition, our learning-based models outperform the state-of-the-art dynamic saliency models.
Most existing bottom-up saliency models use contrast features to determine the saliency in an image.
Using several state-of-the-art bottom-up visual saliency models that measure local and global spatial image outliers, we show that maximum saliency inside the selected object is significantly higher than inside the non-selected object and the background.
The final result combines the 2D saliency maps (from 2D saliency models usually using color contrast, intensity, or image texture) and the depth-saliency maps (DSM).
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