中文摘要：

针对传统基于像素的显著性模型存在的边缘模糊、不适于低对比度环境等问题，提出一种基于双目视觉信息的显著性区域检测方法．采用简单线性迭代聚类（SLIC）方法对图像进行超像素分割，将生成的超像素区域进行合并．通过计算各区域在左右视图的相对移动距离获取物体深度信息，以区域为单位分别计算颜色对比度及深度对比度，进行合成得到区域的显著性值．结果表明，生成的显著性图轮廓清晰，边缘锐利，同等条件下近处及深度变化显著的区域能够获得更高的显著性．该方法符合人类视觉感知特征，适用于移动机器人障碍物检测及场景识别．

英文摘要：

Traditional pixel-based saliency model has some deficiencies, such as poorly defined borders and low performance in low contrast situation. A stereo vision based salient region detection approach was proposed. Simple linear iterative clustering （SLIC） method was adopted to perform superpixel segmentation. Superpixels were merged to construct segmentation image. Depth cue was computed by measuring the distance of region shifts in given stereo pair. For each region, color contrast and depth contrast were computed separately, and then fused to get saliency value. Experimental result shows that saliency map has clear contour and sharp edge and regions at close range or with high depth contrast get more saliency. The proposed method is consistent with human visual perception and suitable for obstacle detection and scene recognition in mobile robot.