融合深度信息的视觉注意模型研究

为了解决基于二维图像的显著性检测方法中出现的光噪声、前景背景相似、多目标遮挡等问题，有效地突出显著区域并抑制背景区域，基于颜色、深度信息提出一种基于深度选择性差异及背景先验的显著性检测模型.首先，根据深度图质量进行颜色以及深度特征所占比例的调节；其次，依据深度图的内在特性，计算图像的基于深度选择性差异的显著性；然后，基于所获取的边界背景集合和基于深度先验的背景集合，计算图像的基于背景先验的显著性；最后，对前期得到的2个显著图进行融合及调整，并对显著图进行优化，得到最终的显著性检测结果.实验结果表明，该模型能较好地反映颜色以及深度信息对显著性检测的影响，计算结果更符合人类视觉.

A model for saliency-based visual attention for rapid scene analysis

Depth information plays an important role in the human visual system. Based on color and depth features, a saliency detection model was proposed in this paper based on depth selective difference and prior background prior. First, the proportion of color and depth features was adjusted according to the quality of depth map. Second, the saliency map based on depth selectivity difference was calculated according to the intrinsic characteristics of depth map.Then, based on the acquired boundary background set and depth prior background set, a saliency detection method was presented based on prior background prior. Finally, the two saliency maps obtained in the previous period were fused and adjusted, and the saliency maps were optimized to obtain the final saliency detection result. Experiments on two publicly available datasets show that the proposed method performs better than that of other state-of-the-art approaches.