{"title":"Multi-layer background sprite model for 2D-to-3D video conversion","authors":"W. Lie, Chih-Hao Hu, Yi-Kai Chen, J. Chiang","doi":"10.1109/APSIPA.2017.8282033","DOIUrl":null,"url":null,"abstract":"This paper presents a technique for semi-automatic 2D-to-3D stereo video conversion, which was known to provide user intervention in segmenting foregrounds and assigning corresponding depth information for key frames and then get the depth maps for other non-key frames via automatic depth propagation. Our algorithm escapes from traditional depth propagation paradigm based on motion estimation and compensation. For foregrounds in non- key frames, object kernels standing for the most confident parts are identified first and then used as the seeds for graph-cut segmentation. Since the graph-cut segmentation for foregrounds is performed independently for each non-key frame, the results will be free of the limitation by objects' motion activity. For backgrounds, all video frames, after foregrounds being removed, are integrated into a common multi-layer background sprite model (ML-BSM) based on image registration algorithm. Users can then draw background depth profiles for the ML-BSM in a video-based manner (not frame-based), thus reducing the human efforts required significantly. Our ML-BSM algorithm is an extension of our prior work, BSM [8], aiming to solve the cases when the foreground and the background have a large depth variation or the camera has a substantial panning/rotating motion. Experiments show that the adoption of multi-layers BSM architecture and iterative foreground refinement based on BSM validation can improve the depth image quality significantly.","PeriodicalId":142091,"journal":{"name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIPA.2017.8282033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a technique for semi-automatic 2D-to-3D stereo video conversion, which was known to provide user intervention in segmenting foregrounds and assigning corresponding depth information for key frames and then get the depth maps for other non-key frames via automatic depth propagation. Our algorithm escapes from traditional depth propagation paradigm based on motion estimation and compensation. For foregrounds in non- key frames, object kernels standing for the most confident parts are identified first and then used as the seeds for graph-cut segmentation. Since the graph-cut segmentation for foregrounds is performed independently for each non-key frame, the results will be free of the limitation by objects' motion activity. For backgrounds, all video frames, after foregrounds being removed, are integrated into a common multi-layer background sprite model (ML-BSM) based on image registration algorithm. Users can then draw background depth profiles for the ML-BSM in a video-based manner (not frame-based), thus reducing the human efforts required significantly. Our ML-BSM algorithm is an extension of our prior work, BSM [8], aiming to solve the cases when the foreground and the background have a large depth variation or the camera has a substantial panning/rotating motion. Experiments show that the adoption of multi-layers BSM architecture and iterative foreground refinement based on BSM validation can improve the depth image quality significantly.