{"title":"增强立体特征,用于移动平台上的建筑立面检测","authors":"J. Delmerico, Jason J. Corso, P. David","doi":"10.1109/WNYIPW.2010.5649753","DOIUrl":null,"url":null,"abstract":"Boosting has been widely used for discriminative modeling of objects in images. Conventionally, pixel- and patch-based features have been used, but recently, features defined on multilevel aggregate regions were incorporated into the boosting framework, and demonstrated significant improvement in object labeling tasks. In this paper, we further extend the boosting on multilevel aggregates method to incorporate features based on stereo images. Our underlying application is building facade detection on mobile stereo vision platforms. Example features we propose exploit the algebraic constraints of the planar building facades and depth gradient statistics. We've implemented the features and tested the framework on real stereo data.","PeriodicalId":210139,"journal":{"name":"2010 Western New York Image Processing Workshop","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Boosting with stereo features for building facade detection on mobile platforms\",\"authors\":\"J. Delmerico, Jason J. Corso, P. David\",\"doi\":\"10.1109/WNYIPW.2010.5649753\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Boosting has been widely used for discriminative modeling of objects in images. Conventionally, pixel- and patch-based features have been used, but recently, features defined on multilevel aggregate regions were incorporated into the boosting framework, and demonstrated significant improvement in object labeling tasks. In this paper, we further extend the boosting on multilevel aggregates method to incorporate features based on stereo images. Our underlying application is building facade detection on mobile stereo vision platforms. Example features we propose exploit the algebraic constraints of the planar building facades and depth gradient statistics. We've implemented the features and tested the framework on real stereo data.\",\"PeriodicalId\":210139,\"journal\":{\"name\":\"2010 Western New York Image Processing Workshop\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Western New York Image Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WNYIPW.2010.5649753\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Western New York Image Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WNYIPW.2010.5649753","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Boosting with stereo features for building facade detection on mobile platforms
Boosting has been widely used for discriminative modeling of objects in images. Conventionally, pixel- and patch-based features have been used, but recently, features defined on multilevel aggregate regions were incorporated into the boosting framework, and demonstrated significant improvement in object labeling tasks. In this paper, we further extend the boosting on multilevel aggregates method to incorporate features based on stereo images. Our underlying application is building facade detection on mobile stereo vision platforms. Example features we propose exploit the algebraic constraints of the planar building facades and depth gradient statistics. We've implemented the features and tested the framework on real stereo data.