Gradient Estimation in Volume Data using 4D Linear Regression

Gradient Estimation in Volume Data using 4D Linear Regression (2000)

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by László Neumann , Balázs Csébfalvi , Andreas König , Eduard Gröller

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Datum	Value	Source
TITLE	Gradient Estimation in Volume Data using 4D Linear Regression	SVM HeaderParse 0.1
AUTHOR NAME	László Neumann	user correction - Legacy Corrections
AUTHOR NAME	Balázs Csébfalvi	user correction - Legacy Corrections
AUTHOR NAME	Andreas König	user correction - Legacy Corrections
AUTHOR AFFIL	Institute of Computer Graphics,; Vienna University of Technology,	user correction - Legacy Corrections
AUTHOR ADDR	Wien, Karlsplatz 13/186/2, A-1040, AUSTRIA	user correction - Legacy Corrections
AUTHOR NAME	Eduard Gröller	user correction - Legacy Corrections
AUTHOR AFFIL	Institute of Computer Graphics,; Vienna University of Technology,	user correction - Legacy Corrections
AUTHOR ADDR	Wien, Karlsplatz 13/186/2, A-1040, AUSTRIA	user correction - Legacy Corrections
ABSTRACT	. In 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. 1 Introduction In direct volume rendering the quality of the generated image is strongly influ...	user correction - Legacy Corrections
YEAR	2000	user correction - Legacy Corrections
CITATIONS	13 found	ParsCit 1.0