Gradient Estimation in Volume Data using 4D Linear Regression

In Section III.B we give a short description of our pixel location algorithm and stereo height estimator, and how we use these algorithms together with the TSX data set and

Figure 3.15 Segments of RADARSAT-2 quad-polarisation data on December 10 th 2009 using Yamaguchi decomposition method: Yamaguchi helix rotated (top left), volume rotated

We fitted multivariable models, using generalized linear (here both logistic and linear) regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine

Figure 3.3: Direct volume rendering of segmented volume data using a style transfer function based on data value and object membership.. Illustration by Bruckner and Gröller

Using templates and shearing/de-fanning of beams, we fetch 2D planes from the volume dataset and perform sheared tri-linear interpolation between discrete neighboring

timation techniques or for zero-order central difference gradients. Both the sheared tri-linear interpolation and the ABC gradient estimation method do not require

Figure 4 shows the tooth data set rendered with gradient- magnitude opacity-modulation, direct volume rendering us- ing a clipping plane, and context-preserving volume render- ing

We show that the importance information acquired with an eye tracker can be used to choose view- point, volume center, and rendering degrees.. We believe that this