Master's Thesis - Studierstube Augmented Reality Project - Graz ...

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2.1 Technical Visualization Figure 2.11: An exemplary color gradient for arbitrary parameter value mapping. A gradient like this can even be used to suppress certain flow properties if for example an appropriate transparency-value definition can be performed. The main visualization techniques for any kind of flow data can be divided into four main classes. A related taxonomy can be found in [Weiskopf2007] which we summarized in figure 2.12. Furthermore some of these techniques are only valid or behave differently for steady or unsteady flow and can thus be subdivided again. Steady flow refers in this context to unchangeable vector fields and unsteady flow to changeable fields over time. • point-based direct flow visualization implies that some fixed geometry is rendered for each velocity vector. A large viewport showing enough of these representatives enables the observer to interpret the whole vector field. The problem of visual clutter and occlusion is evident for volumetric datasets. • sparse particle tracing techniques relies on calculations done based on the movement of massless particles injected into the field and envelop everything from concrete particle effects to complex trajectory integrations. • dense particle tracing techniques mostly rely on a specialized convolution of a texture with an even volumetric flow field. Again, strategies to cope with visual clutter and occlusions have to be investigated. • feature based visualization approaches 20
2.1 Technical Visualization try to extract desired patterns like vortices from the raw vector field to provide an abstract representation of the contained important information. These are the most complex methods since almost a kind of artificial intelligence has to be developed to find out the important parts of the data. Similar algorithms can be found in the field of computer vision. 21
Page 1 and 2: Master’s Thesis Quantitative Meas
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Page 5 and 6: Kurzfassung In dieser Arbeit werden
Page 7 and 8: CONTENTS 4.2.2 Noise Suppression .
Page 9 and 10: Chapter 1 Introduction Assessment o
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Page 13 and 14: Chapter 2 Visualization Visualizati
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Page 49 and 50: 3.2 Excitation of Magnetized Matter
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4.4 Visualization Framework impleme
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4.4 Visualization Framework hardwar
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4.4 Visualization Framework 3 void
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4.4 Visualization Framework • fra
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4.4 Visualization Framework Figure
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Chapter 5 Implementation This chapt
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5.2 iMEDgine extensions Figure 5.1:
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5.2 iMEDgine extensions which eases
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5.2 iMEDgine extensions solution fu
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5.2 iMEDgine extensions 23 cam_posi
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5.2 iMEDgine extensions Figure 5.4:
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5.2 iMEDgine extensions • SoSFStr
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5.2 iMEDgine extensions 6 Z=" -238.
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5.3 Visualization nodes and subgrap
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Chapter 6 Experiments Several atten
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6.3 Results 6.3 Results To complete
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6.4 Scalability activity. The highe
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6.4 Scalability (a) Stream lines fo
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6.4 Scalability (a) Stream tubes ap
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6.4 Scalability Figure 6.10: Compar
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Chapter 7 Future Work This chapter
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7.3 Derived Magnitudes possibilitie
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7.5 Clinical Evaluations (a) Stereo
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Beside the improvement of known spa
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Appendix A Intermediate File Format
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A.2 Data Files Offset Name Type Des
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This chapter supplements chapter 5
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143 Figure B.3: An thinned out UML
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Figure B.5: This UML class diagram
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Glossary Notation bipolar gradient
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Glossary Notation Description MIP M
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Glossary Notation Description shade
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LIST OF FIGURES 3.5 The slice-profi
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List of Tables 2.1 CPU - GPU analog
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REFERENCES [Brill1994] M. Brill, W.
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REFERENCES [iMedgine2006] T. Gross,
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REFERENCES [Kniss2002a] Joe Kniss,
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REFERENCES [Markl2003] Michael Mark
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REFERENCES [Roth1982] Scott D. Roth
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REFERENCES [Vivek1999] andAlexPang
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INDEX flow data, 135 format definit
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Master's Thesis - Studierstube Augmented Reality Project - Graz ...

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