Stereo GrabCut: Interactive and Consistent Object Extraction for Stereo Images

This paper presents an interactive object extraction approach for stereo images. The extraction task on stereo images has two significant differences compared to that on monoscopic images. First, the segmentation for both images should be consistent. Second, stereo images have implicit depth information, which supplies an important cue for object extraction. In this paper, we generate consistent segmentation by putting the correspondence relationship in a graph cut framework. Besides, we leverage depth information, which is obtained by stereo matching, to give a pre-estimation of foreground and background. The pre-estimation is then used to generate accurate color models to perform a graph cut based segmentation. To simplify the user interaction, we supply an interface similar to GrabCut, which only needs the user to drag a compact rectangle in most cases. The experiments show our approach works fast and produces more satisfactory results than state-of-the-art.