Automatic segmentation fusing color and depth

Graph-based segmentation requires interactive input, and color segmentation poses challenges when foreground has similar color with background. This paper presents an automatic robust segmentation fusing color and depth. A saliency detection algorithm based on visual attention is proposed. Depth is firstly used as a filter to limit object pixel candidates, color is used to further detect object region by saliency calculation. The saliency detection algorithm is incorporated into graph-based segmentation by automatically setting the detected range as uncertain pixel set. Our segmentation algorithm uses color as main information to set energy terms of graph function, and uses depth as supplemental information to adjust these terms basing on consistency decision of color and depth. Experimental results demonstrate that the proposed segmentation enhances the performance using color alone, and realizes fully automation.