{"title":"Fast interactive segmentation in stereo images based on multi-scale graph","authors":"Wei Ma, Xiaohui Qiu, Luwei Yang, Shuo Liu, Lijuan Duan","doi":"10.1109/ACPR.2015.7486505","DOIUrl":null,"url":null,"abstract":"It is hard for current interactive stereo image segmentation methods to deal with large scale images with fast feedback after each interaction. In this paper, we present an interactive stereo image segmentation method. Different from current methods, our method introduces a multi-scale graph structure for fast graph cut optimization. Besides, we use GPU parallel computing to handle single instruction multiple data tasks involved in the segmentation. Compared with state-of-the-art methods, our approach significantly accelerates segmentation speed. In the meanwhile, our method obtains segmentation accuracy comparable with state-of-the-art methods.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2015.7486505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
It is hard for current interactive stereo image segmentation methods to deal with large scale images with fast feedback after each interaction. In this paper, we present an interactive stereo image segmentation method. Different from current methods, our method introduces a multi-scale graph structure for fast graph cut optimization. Besides, we use GPU parallel computing to handle single instruction multiple data tasks involved in the segmentation. Compared with state-of-the-art methods, our approach significantly accelerates segmentation speed. In the meanwhile, our method obtains segmentation accuracy comparable with state-of-the-art methods.