{"title":"Iso-contour volume rendering","authors":"J. Arvo, K. Novins","doi":"10.1145/197938.197977","DOIUrl":null,"url":null,"abstract":"In this paperwe presenta new approach to volume rendering in which curves of constant intensity in image space,or iso-contours, are computed directly for eachview. The generated iso-contours can be used to drive various visualization and feature-detection algorithms. The approach imposes no restriction on the organization of the data points and can accommodatea large class of radially-symmetric filter functions. The technique works well for both perspective and orthographic viewing projections. Each iso-contour is definedby an ordinary differential equation, which is solved numerically using a predictor-corrector method. A key element of the algorithm is the use of image intensity gradients, which we computeby means of a closedform expression that holds at every point on the image plane. A caching algorithm is described that dramatically accelerates the gradient computations on large datasets. The algorithm is demonstrated on emission-only datasets. We conclude by describing a number of possible enhancements.","PeriodicalId":124559,"journal":{"name":"Symposium on Volume Visualization","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symposium on Volume Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/197938.197977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paperwe presenta new approach to volume rendering in which curves of constant intensity in image space,or iso-contours, are computed directly for eachview. The generated iso-contours can be used to drive various visualization and feature-detection algorithms. The approach imposes no restriction on the organization of the data points and can accommodatea large class of radially-symmetric filter functions. The technique works well for both perspective and orthographic viewing projections. Each iso-contour is definedby an ordinary differential equation, which is solved numerically using a predictor-corrector method. A key element of the algorithm is the use of image intensity gradients, which we computeby means of a closedform expression that holds at every point on the image plane. A caching algorithm is described that dramatically accelerates the gradient computations on large datasets. The algorithm is demonstrated on emission-only datasets. We conclude by describing a number of possible enhancements.