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To compare the predictions based on saliency maps to human performance, we used the dataset of human gaze in free-viewing static natural scenes collected from 20 human subjects in free-viewing 120 images by Bruce and Tsotsos [30].
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Other image-resizing algorithms [15, 16], based on saliency map, detected and transmitted the most important regions to the small display device, where users can browse the important regions through scrolling the pages, but the important regions could not be seen at the same time.
We would like to explore automatic markup based on saliency points or corner detection.
To test this hypothesis, we used the procedure shown in Fig. 7 to compute saliency maps of a set of natural scenes and compared the predictions based on the saliency maps to human gaze in free-viewing these scenes.
Then the semantic region-of-interest (ROI) is extracted based on the saliency maps of SVA.
Predictions based on automatic saliency maps made by computers would then be possible to make.
Then, a semantic region-of-interest (ROI) is extracted based on the saliency maps of SVA.
The region of interest (ROI) for objects is obtained based on the saliency map and the raw probability map.
In the fourth part, the focus of the attention area is located based on the saliency map.
Furthermore, the proposed method acknowledges the possibility of improving image quality and simultaneously reducing execution time, based on the saliency map.
Based on the saliency map approach of [ 16], a POMDP formalism is used in [ 35, 36] to optimize gaze target selection based on the detections arising from previous gaze targets, visual saliency and global scene priors.
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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