StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images

Ran Ju, Tongwei Ren, Gangshan Wu; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1724-1732

Consistent object extraction plays an essential role for stereo image editing with the population of stereoscopic 3D media. Most previous methods perform segmentation on entire images for both views using dense stereo correspondence constraints. We find that for such kind of methods the computation is highly redundant since the two views are near-duplicate. Besides, the consistency may be violated due to the imperfectness of current stereo matching algorithms. In this paper, we propose a contour based method which searches for consistent object contours instead of regions. It integrates both stereo correspondence and object boundary constraints into an energy minimization framework. The proposed method has several advantages compared to previous works. First, the searching space is restricted in object boundaries thus the efficiency significantly improved. Second, the discriminative power of object contours results in a more consistent segmentation. Furthermore, the proposed method can effortlessly extend existing single-image segmentation methods to work in stereo scenarios. The experiment on the Adobe benchmark shows superior extraction accuracy and significant improvement of efficiency of our method to state-of-the-art. We also demonstrate in a few applications how our method can be used as a basic tool for stereo image editing.