Furthermore, the phase spectrum of quaternion Fourier transform (PQFT) is applied to detect the spatiotemporal saliency in the dynamic scenes.
It would be ideal to analyze the saliency for each object as a whole.
In order to detect the salient objects with different sizes, the saliency support region is explored and the saliency value of the center pixel of the region is obtained.
This leads to a temporal saliency map which can detect the moving salient objects clearly.
The resulting saliency maps are often overemphasize small, purely local features, and fail to detect the internal part of the target, which makes the approach less useful for applications, such as segmentation and detection.
Some application areas, ranging from computer vision, computer graphics, to multimedia processing, can benefit from saliency detection, considering that the detected saliency has depicted the visual importance of different areas of the visual stimuli.
The saliency based method [ 102] first detects the salient regions and then classifies them as blood vessel, hard exudates or drusen.
Therefore, it can be concluded that the proposed model detects the SVA accurately and simulates HVS well by fusing depth information, static image saliency, and motion saliency.
Furthermore, we compute the visual saliency based on principles 1 and 2. Some methods detect saliency at a single spatial scale [33, 35], while others combine feature maps at multiple scales to the final saliency map [1, 19, 20].
Through this fusion, the saliency value for each pixel is calculated based on multi-scale saliency and its texture information.
