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Kurzfassung
Contents
Title page
Contents
Introduction
Volume visualization
Volume reconstruction
Thesis outline
State of the Art
The early years: 1D signal processing
The eighties: an image processing view
Eventually going to 3D
How to reconstruct a 3D function from a set of samples
3D derivative reconstruction
Miscellaneous reconstruction methods
Sampling
Important functions in signal processing
The ramp and step signals
The impulse signal
The impulse train
The Fourier transform
Fourier transforms of base functions
Convolution
The convolution theorem
The process of sampling
Summary and conclusions
Reconstruction
The sampling theorem
Ideal reconstruction
Reconstruction in practice
Nearest neighbor interpolation
Linear interpolation
Symmetric cubic filters
Windowed sinc function
Interpolation using zero insertion
Evaluation of filters in spatial domain
Summary and conclusions
Derivative reconstruction
Gradient reconstruction
Central and intermediate differences
Cubic spline derivatives
Windowed cosc function
An empirical experiment (actually two)
Curvature reconstruction
Ideal reconstruction and windows
Cubic spline derivatives of order two
Summary and conclusions
Discrete filters
Implementation scheme
Discrete Filters in Frequency Domain
Discrete Filters in Spatial Domain
Results
Summary and conclusions
Implementation
Summary
Introduction
Previous work
Sampling
Reconstruction
Reconstruction in practice
Interpolation using zero insertion
Evaluation of filters in spatial domain
Derivative reconstruction
Discrete filters
Conclusions
Conclusions in general
Future work
Glossary
Windows and frequency responses
First derivative reconstruction results
Bibliography
Acknowledgments
1999-12-29