Visual data analytics at TU München

Flashcards and summaries for Visual data analytics at the TU München

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Study with flashcards and summaries for the course Visual data analytics at the TU München

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

 Difference between Curvelinear and unstructured grid?

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Name an algorithm that is commonly used in INDIRECT volume visualization.

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Isolines properties

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Explain the filtering/enhancement stage. Give at least two examples.

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

In which stage of the visualization pipeline are the viewpoint and

lighting parameters specified?

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

In which stage of the visualization pipeline are colors assigned to every voxel?

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Rendering (4th step)

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

What are the independent and dependent variables in a 3D spatial curve

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

What are the independent and dependent variables in a 3D vector field?

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

How is a cartesian grid different from

a regular grid?

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Discuss independent vs. dependent variables in data. Give at least two examples each.

Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Rectilinear Grid - difference to cartesian or regular

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Exemplary flashcards for Visual data analytics at the TU München on StudySmarter:

Visual data analytics

 Difference between Curvelinear and unstructured grid?

Unstructered Grid you also need to save neighborhood points

The cells are tetrahedra or hexahedra

Visual data analytics

Name an algorithm that is commonly used in INDIRECT volume visualization.

MC-algorithm (Marching Cubes)

Visual data analytics

Isolines properties

  • Isolines are always closed curves
  • They never intersect with each other and they don’t self-intersect
  • Always orthogonal to scalar field’s gradient 

Visual data analytics

Explain the filtering/enhancement stage. Give at least two examples.

It is a process to obtain useful data (e.g., 3D volume) 

Examples:

  • Conversion of data format  
  • Co-registration of different data sets 
  • Interpolation/approximation of missing values 
  • Remove nonrelevant data
  • Cleaning and denoising 

Visual data analytics

In which stage of the visualization pipeline are the viewpoint and

lighting parameters specified?

Rendering

Visual data analytics

In which stage of the visualization pipeline are colors assigned to every voxel?

Visualization mapping

Visual data analytics

Rendering (4th step)

Image, video or something else as visualisation

  • Decide viewpoint
  • Visibility calculation
  • Shading/illumination so it looks natural 

Visual data analytics

What are the independent and dependent variables in a 3D spatial curve

???

Visual data analytics

What are the independent and dependent variables in a 3D vector field?

??

Vectors – dependent 

Visual data analytics

How is a cartesian grid different from

a regular grid?

dx the same as dy since it is  evenly spaced in all direction.

A regular grid does not need to have it, 

Visual data analytics

Discuss independent vs. dependent variables in data. Give at least two examples each.

Independent variables

Dimension of the domain of the problem

  • Time
  • 2D/3D space


Dependent variables

Type and dimension of the data

  • Temperature
  • Density
  • Values
  • Velocity vectors

Visual data analytics

Rectilinear Grid - difference to cartesian or regular

Varying samples of distances between dx and dy, 

We can no longer compute the positions easily so we need to save them 

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