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In the second experiment, observers were asked to draw a polygon circumscribing the most salient object in cluttered scenes.
However, to improve image retrieval accuracy, considering only the most salient object in an image is insufficient because the background also influences the accuracy of image retrieval.
It can be seen that most of the methods can detect the single salient object in a simple scene, such as the second row.
To test how well the performance of our proposed method generalizes to a different dataset for detecting salient object in RGB-D images, we evaluate it on the NJU-DS400[10].
However, it is unsuitable for the SAR image target detection because the targets in the SAR image are weaker and smaller than the salient object in the optical image; the targets in the SAR image become fuzzy and disappear rapidly when the resolution decreases.
In these circumstances, search is for the most salient object in the search display, and capture does not occur (Theeuwes, 1992).
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This enables the method to achieve good robustness and precision in identifying salient objects in videos under various challenging conditions.
Therefore, in this paper we propose a novel spatiotemporal saliency model to estimate salient objects in unconstrained videos.
The method introduces saliency enhancement to enhance the landslide regions, making the landslides salient objects in the image.
In this work, we propose a novel graphic saliency detection method to detect visually salient objects in images rendered from 3D geometry models.
Through experiments on two challenging datasets, the proposed model consistently outperforms the state-of-the-art models for popping out salient objects in unconstrained videos.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com