Interactive stereo image segmentation via adaptive prior selection | |
Ma, Wei1; Qin, Yue1; Xu, Shibiao2; Zhang, Xiaopeng2 | |
刊名 | MULTIMEDIA TOOLS AND APPLICATIONS |
2018-11-01 | |
卷号 | 77期号:21页码:28709-28724 |
关键词 | Stereo image segmentation Interactive segmentation Prior selection Multi-label MRF Graph cut |
ISSN号 | 1380-7501 |
DOI | 10.1007/s11042-018-6067-5 |
通讯作者 | Xu, Shibiao(shibiao.xu@ia.ac.cn) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn) |
英文摘要 | Interactive stereo image segmentation (i.e., cutting out objects from stereo pairs with limited user assistance) is an important research topic in computer vision. Given a pair of images, users mark a few foreground/background pixels, based on which prior models are formulated for labeling unknown pixels. Note that color priors might not help if the marked foreground and background have similar colors. However, integrating multiple types of priors, e.g., color and disparity in segmenting stereo pairs, is not trivial. This is because differing pairs of images and even differing pixels in the same image might require different proportions of the priors. Besides, disparities of natural images are too noisy to be directly used. This paper presents a method that can adaptively determine the proportion of the priors (color or disparity) for each pixel. Specifically speaking, the segmentation problem is defined in the framework of MRF (Markov Random Field). We formulate an MRF energy function which is composed of clues from the two types of priors, as well as neighborhood smoothness and stereo correspondence constraints. The weights of the color and disparity priors at each pixel are treated as variables which are optimized together with the label (foreground or background) of the pixel. In order to overcome the noise problem, the weight of the disparity prior is controlled by a confidence value learned from data. The energy function is optimized by using multi-label graph cut. Experimental results show that our method performs well. |
资助项目 | National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61379096] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61502490] ; Scientific Research Project of Beijing Educational Committee[KM201510005015] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[4152006] ; Beijing Municipal Natural Science Foundation[4152006] |
WOS关键词 | GRAPH CUTS |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | SPRINGER |
WOS记录号 | WOS:000446601500038 |
资助机构 | National Natural Science Foundation of China ; Scientific Research Project of Beijing Educational Committee ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Beijing Municipal Natural Science Foundation |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/23049] |
专题 | 多媒体计算与图形学团队 |
通讯作者 | Xu, Shibiao; Zhang, Xiaopeng |
作者单位 | 1.Beijing Univ Technol, Fac Informat Technol, 100 Pingleyuan St, Beijing 100124, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Ma, Wei,Qin, Yue,Xu, Shibiao,et al. Interactive stereo image segmentation via adaptive prior selection[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(21):28709-28724. |
APA | Ma, Wei,Qin, Yue,Xu, Shibiao,&Zhang, Xiaopeng.(2018).Interactive stereo image segmentation via adaptive prior selection.MULTIMEDIA TOOLS AND APPLICATIONS,77(21),28709-28724. |
MLA | Ma, Wei,et al."Interactive stereo image segmentation via adaptive prior selection".MULTIMEDIA TOOLS AND APPLICATIONS 77.21(2018):28709-28724. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论