Gradient Estimation in Volume Data using 4D Linear Regression

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dc.contributor.authorNeumann, Laszloen_US
dc.contributor.authorCsebfalvi, Balazsen_US
dc.contributor.authorKonig, Andreasen_US
dc.contributor.authorGroller, Eduarden_US
dc.date.accessioned2015-02-16T09:52:33Z
dc.date.available2015-02-16T09:52:33Z
dc.date.issued2000en_US
dc.description.abstractIn this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore this technique can be used for surface smoothing and gradient estimation at the same time.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume19en_US
dc.identifier.doi10.1111/1467-8659.00427en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages351-358en_US
dc.identifier.urihttps://doi.org/10.1111/1467-8659.00427en_US
dc.publisherBlackwell Publishers Ltd and the Eurographics Associationen_US
dc.titleGradient Estimation in Volume Data using 4D Linear Regressionen_US
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