DC FieldValueLanguage
dc.contributor.authorJirka, Tomáš
dc.date.accessioned2016-06-24T08:10:30Z
dc.date.available2016-06-24T08:10:30Z
dc.date.issued2003
dc.identifier.urihttp://www.kiv.zcu.cz/publications/
dc.identifier.urihttp://hdl.handle.net/11025/21609
dc.format84 s.
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of West Bohemia in Pilsenen
dc.rights© University of West Bohemia in Pilsenen
dc.subjectmultidimenzionální datacs
dc.subjectvizualizacecs
dc.titleMultidimensional data visualization: technical report no. DCSE/TR-2003-03en
dc.typezprávacs
dc.typereporten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedMultidimensional volumetric data are often used for storing information in various fields of science such as physics, astronomy etc. because they best match the character of the underlying phenomena. The information contained in such data is, however, usually very dense and thus difficult to understand without subsidiary tools. Proper visualization is definitely one of the most effective and crucial ones. Multidimensional data can be sorted according to various criteria. First, it is the domain, over which the data are defined, and which is usually two or three dimensional. Second, it is the dimension of the data values themselves, which is theoretically unlimited and depends on the application. Two or three dimensional vector fields can be encountered most frequently, but fields of quadratic tensors are also quite common. It is, however, necessary to realize, that the character of the data must be taken into account as well. Three dimensional vectors need to be treated in a different way than a set of three scalar values. The third important criterion is, whether the data vary in time. If so, they are usually called time dependent. Otherwise, we speak of time independent data. Such a variety of kinds of data implies even larger variety of visualization techniques. These may be again divided into categories according to various criteria. Obviously, the type of data to apply the particular technique to is the primary one. Among the secondary criteria e.g. the following aspects may belong; whether the approach focuses on the whole data set or just certain region, whether it visualizes t he actual data or some derived quantities (e.g. velocity magnitudes, gradients and other), whether it aims to be “photo-realistic” or not et cetera. This report aims to bring a summary of existing approaches that deal with multidimensional data visualization and to describe selected methods in detail. It should also introduce our previous work, which focused especially on isosurface extraction and gradient estimation, and present the goals of our future research.en
dc.subject.translatedmultidimesional dataen
dc.subject.translatedvisualizationen
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