{"title":"Shape-from-texture by wavelet-based measurement of local spectral moments","authors":"B. Super, A. Bovik","doi":"10.1109/CVPR.1992.223260","DOIUrl":null,"url":null,"abstract":"A closed-form solution to the problem of computing 3D curved-surface shape from texture cues is presented. An expression showing the dependence of localized image spectral moments on localized surface spectral moments and on local surface orientation is derived. The local image spectra are measured with wavelets, and the expression is solved for the surface orientation at each point. Because the method uses localized spectral information, it operates at a very low level in the visual hierarchy. No extraction of texture or edge elements is required. The wavelet-based computation used is biologically plausible, easily parallelized for rapid computation, and has been shown to be the basis for effective solutions to a variety of other vision tasks. The method is demonstrated on a number of real-world examples.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.1992.223260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39
Abstract
A closed-form solution to the problem of computing 3D curved-surface shape from texture cues is presented. An expression showing the dependence of localized image spectral moments on localized surface spectral moments and on local surface orientation is derived. The local image spectra are measured with wavelets, and the expression is solved for the surface orientation at each point. Because the method uses localized spectral information, it operates at a very low level in the visual hierarchy. No extraction of texture or edge elements is required. The wavelet-based computation used is biologically plausible, easily parallelized for rapid computation, and has been shown to be the basis for effective solutions to a variety of other vision tasks. The method is demonstrated on a number of real-world examples.<>