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Issues to discussQlikview assignment - need more help?Set up a lab session, probablyParaview: 8 reports has been received
Exam to be published tomorrow(29/4 and delivered by 16 May)
Free lab to be discussed this afternoon
Schedule 12/5 to be revised
Lecture tomorrow, visualization ofuncertainty (17-19), Kai-Mikael Jää-Aro
Data Visualization, TeleaIntroductionFrom graphics to visualizationData representationThe visualization pipelineScalar visualizationVector visualizationTensor visualization
Data Visualization, TeleaDomain modeling techniquesImage visualizationVolume visualizationInformation visualizationConclusionsVisualization software
Possible exam question• Give at least five different examples of
questions you may ask when examining adata set
• Describe with example figures what featuresthat are important from computer graphicswhen working with visualization.
• Describe how one can convert a data typefrom xxx to yyy….
• Discuss different aspects of differentcolopmaps, such as luminance map andrainbow colormap
• Give three examples of the use of glyphs invisualization
8. Domain modeling techniques
Techniques used in visualization
Such as cutting, slicing, selection, gridtechniques, decimation techniques,triangulation, surface reconstruction,
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Triangulation methods
Both in 2D and 3D
Surface reconstruction and rendering Grid simplification - decimation
Grid refinement - inserting more grid pointsExample of exam question, sketch
Describe an algorithm to do decimation of agrid. Why do you want to do decimation andwhat are the drawbacks with decimation?
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9. Image visualization This is mainly on image processing with is aperipheral part of this course although it is importantto have some knowledge on this area.
10. Volume visualization
Several techniques for data acquisition (CT,MRI, PET, SPECT,…)Often hugh amounts of data.
Ray casting
There are different ways of combining the valuesalong the ray
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Sketch of possible exam questions
Describe different ways of combining the valuesalong a ray when working with ray casting.
Describe different techniques to visualizevolume data
Image order and object order techniques
Image order:For all pixels in image plane {compute valuealong ray passing through pixel}
Object order:For all voxels {compute contributions to image}
11. Information visualization A good summary of Gustavs lectures,an example - parallel coordinates, interactive use:
12. Conclusions
Some concluding comments especially on information visualization versusscientific visualization
AppendixA. Visualization software
Scientific visualization software:Vtk, AVS/Express, IRIS Explorer, SCIRun,Paraview, MayaViMedical imaging software:Insight toolkit (ITK), 3D Slicer, TeemInformation visualization software:Infovis Toolkit (IVTK), Prefuse, GraphViz,Tulip, Treemap, XmdvTool
Home exam, DD1257, Visualization, published 11 May 2009Please send your answers as a pdf or word-file (not docx)to [email protected] not later than 27 May.The answer to a 2p-question is expected to be something like half a page.The important issues in this exam are concepts. The exam is thereforenot on vtk.You should use your own words to answer the questions.Copying of text from books, www or the answers from other participantsetc is not allowed. Just using key words or or making tables will usuallynot be accepted. Explain what you mean and the concepts you discuss.Always try to use figures/diagrams to illustrate your answer (if youhave not made them yourself, please give a reference to the source).
The home exam is followed by an oral exam to be booked by emailwith me from a list of time slots given on the web page. The schedule for oral exams will start at 26 May and continue until19 June.The grading on the exam will be given based on the quality of thewritten answers and the oral follow-up.
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(3p) 1. Explain and give examples of the following informationvisualization techniques/diagrams: star plot, parallel coordinates,scatter plots, tree map, cone tree. Try to say what they are supposedto illustrate.
(2p) 2. Describe at least three different examples of the use ofglyphs to visualize uncertainty in data.
(2p) 3. Describe what a transfer function is and how it can be usedto extract different features from 3D volume data. Use figures toexplain. Sometimes it may be hard to extract a feature/material asone would desire. Explain why.
(3p) 4. Describe the marching cubes algorithm and the dividingcubes algorithm and compare them with each other. What are thesimilarities and the differences?
(2p) 5. How and when can we make use of preattentive processingin visualization? Give examples.
(3p) 6. Describe three different ways of visualizing a medical dataset in 3D. Discuss for each alternative the advantages and drawbackswith the way of visualization.
(2p) 7. Describe two different decimation techniques for polygonrepresentation of a surface. How can we judge the quality of adecimation technique?
(2p) 8. Polygn data sets are often produced without point normalsbeing defined.(1p) a. Why would you want a point normal in visualization?(2p) b. Describe the steps needed to generate point normals (usefigures and describe what consideration you have to make).(1p) c. What happens if the feature angle is too big?
(5p) 9. Discuss how air pollution in a city like Stockholm can bevisualized. Assume that measures of pollutions have been done ata number of key positions. Make sure that your visualization cangive answers to the following questions: - Which roads are most polluted? - Under what time slices during a period of 24 hours are theremaxima and minima in pollution? - What variations of pollutions can be found during a period ofone year?Draw a sketch illustrating the answer to each of the three questionsabove.
Perception in VisualizationChristopher G. Healey
http://www.csc.ncsu.edu/faculty/healey/PP/index.html
Visualization
• Some patterns are easily detected(preattentively)
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Depth perception• Hidden surface removal• Perspective projection• Intensity depth cueing• Shades• Motion• Shadows• Textures• Height/elevation (above ground/ horizon)• Size and elevation• Stereo
Change blindnessan example of our lack of
detecting changes