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Compressive sampling is a novel framework in signal acquisition and reconstruction, which achieves sub-Nyquist sampling by exploiting the sparse nature of most signals of interest. In this letter, we propose a saliency-based compressive sampling scheme for image signals. The key idea is to exploit the saliency information of images, and allocate more sensing resources to salient regions but fewer...
Using a novel wide-view visual presentation system for vision research developed by us and visual retinotopic mapping functional magnetic resonance imaging (fMRI). We have investigated the variation of BLOD response, in three visual areas (V1–V3), as a function of visual retintotopic areas and five contrast levels (96%, 48%, 24%, 12% and 6%). Each visual area was identified in each subject using a...
This paper proposes a saliency-based attention model based on pulsed cosine transform that simulates the lateral surround inhibition of neurons with similar visual features. The model can be extended to Hebbian-based neural networks. The visual saliency can be represented in binary codes, which agrees with the firing pulse of neurons in human brain. In addition, motion saliency can be directly generated...
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