Classical saliency-based visual attention models are adapted for embedding real-time systems with less time and space costs based on approximate Gaussian pyramids of the input image.
利用输入图像的近似高斯金字塔，将经典的基于显著性的视觉注意模型改造为时空开销更小的版本，从而使其更加适合在嵌入式实时系统中实现。
A new visual attention model used for rapid perception of complex targets in natural scene is proposed. In the learning process, the model extracts saliency blobs from a given target's image.
针对自然场景图像中复杂结构目标的快速定位问题，提出一种新的视觉注意模型。
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