{"title":"Colour photometric stereo: simultaneous reconstruction of local gradient and colour of rough textured surfaces","authors":"S. Barsky, M. Petrou","doi":"10.1109/ICCV.2001.937681","DOIUrl":null,"url":null,"abstract":"Classification of a rough 3D surface from 2D images may be difficult due to directional effects introduced by illumination. One possible way of dealing with the problem is to extract the local albedo and gradient surface information which do not depend on the illumination, and classify the texture directly using these intrinsic characteristics. In this paper we present an algorithm for simultaneous recovery of local gradient and colour using multiple photometric images. The algorithm is proven to be optimal in the least squares error sense. Experimental results with real images and comparison with other approaches are also presented.","PeriodicalId":429441,"journal":{"name":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.2001.937681","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
Classification of a rough 3D surface from 2D images may be difficult due to directional effects introduced by illumination. One possible way of dealing with the problem is to extract the local albedo and gradient surface information which do not depend on the illumination, and classify the texture directly using these intrinsic characteristics. In this paper we present an algorithm for simultaneous recovery of local gradient and colour using multiple photometric images. The algorithm is proven to be optimal in the least squares error sense. Experimental results with real images and comparison with other approaches are also presented.