Scientific Visualization

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Credits
6
Types
Specialization complementary (Computer Graphics and Virtual Reality)
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
In this subject, basic knowledge of data visualization, visualization of information and scientific visualization will be given.
We will discuss the following topics:
- Display pipeline.
- Perception in visualization.
- Basic techniques for data visualization.
- Medical data visualization.
- Visualization of molecules.

Teachers

Person in charge

  • Pere Pau Vázquez Alcocer ( )

Others

  • Isabel Navazo Alvaro ( )

Weekly hours

Theory
2
Problems
1
Laboratory
1
Guided learning
0.5
Autonomous learning
8.3

Competences

Technical Competences of each Specialization

Computer graphics and virtual reality

  • CEE1.1 - Capability to understand and know how to apply current and future technologies for the design and evaluation of interactive graphic applications in three dimensions, either when priorizing image quality or when priorizing interactivity and speed, and to understand the associated commitments and the reasons that cause them.
  • CEE1.3 - Ability to integrate the technologies mentioned in CEE1.2 and CEE1.1 skills with other digital processing information technologies to build new applications as well as make significant contributions in multidisciplinary teams using computer graphics.

Generic Technical Competences

Generic

  • CG3 - Capacity for mathematical modeling, calculation and experimental designing in technology and companies engineering centers, particularly in research and innovation in all areas of Computer Science.

Transversal Competences

Entrepreneurship and innovation

  • CTR1 - Capacity for knowing and understanding a business organization and the science that rules its activity, capability to understand the labour rules and the relationships between planning, industrial and commercial strategies, quality and profit. Capacity for developping creativity, entrepreneurship and innovation trend.

Information literacy

  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.

Objectives

  1. By the end of the course, students should be able to know the main concepts behind visualization and representation of volume models in scientific applications (mainly in medical applications). More specifically they will be able to undestand and program algorithms for:
    Related competences:

Contents

  1. Introduction to volume visualizacion
    Presentation of basic principles of volume modeling and visualization, the visualization pipeline and some scientific applications.
  2. Volume data representation
    Presentation and discussion of discrete volume respresentation and interpolation and filtering techniques.
  3. Iso-surface extraction
    Presentation of the main algorithms for extracing iso-surfaces from a scalar volume data-set. Marching-cubes based techniques.
  4. Volume visualization
    Presentation of the main algorithms of direct volume rendering, including 3D textures and ray-casting. Transfer fuctions. GPU-based ray-casting. Introduction to vector field visualization.
  5. 3D Medical Imaging
    Presentation of acquisition techniques (CT, MRI,...), basic segmentation algorithms, fusion of medical data. Applications.
  6. Large Volume Data
    Difficulties for rendering and representing current volume datasets in GPUs. Presentation of some proposed solutions: multiresolution representations, wavelets, compression algorithms, thin-client approaches. Visualization in mobile devices.

Activities

Activity Evaluation act


Lectures

Material will be presented in lectures along the term. You are expected to conduct complementary readings to be presented at a later date or turned in.

Theory
30h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h

Implementation of selected algorithms

A selection of relevant algorithms will be assigned to implement in Lab sessions and on your own, in VTK and C++. You may be required to present your solution in class.

Theory
0h
Problems
0h
Laboratory
15h
Guided learning
0h
Autonomous learning
45h

Lab project(s)

The students will have to complete a lab project that includes two or more practical works that consist in implementing some of the techniques developed in the lectures. This project will be either be presented and discussed at a later date or turned in for grading.

Theory
0h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
20h

Final Exam

At the end of the term, the students will have a final exam, which may be a take-home,

Week: 18
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

The professor provides theoretical lectures where the most important concepts are introduced; moreover supplement material will be provided.
During the laboratory class, the students will receive the guidelines for the analysis and implementation of their programming assignments and will have time to work in their assignments with the teacher supervision when needed.

Evaluation methodology

The students will be marked for their attendance and participation in class (including the presentation of papers and their discussion), yielding a mark "Paper".

Another grade will stem from the student's implementations of selected algorithms (which may include the presentation of their solution in a laboratory class), yielding a mark "Lab".

Finally, students will receive a third mark based on their performance in the final exam, yielding "Exam".

The final grade for the course will be computed as:

Final Grade = 0.2 Paper+ 0.6 Lab + 0.2 Exam

Bibliography

Basic:

Complementary:

  • A Survey of Volumetric Illumination Techniques for Interactive Volume Rendering - Daniel Jönsson, Erik Sundén, Anders Ynnerman, Timo Ropinski , Computer Graphics Forum , 2013.
  • The Visualization Toolkit : an object-oriented approach to 3D graphics - Schroeder, Will; Martin, Ken; Lorensen, Bill, Kitware , cop. 2002. ISBN: 978-1930934078
    http://cataleg.upc.edu/record=b1244232~S1*cat

Web links

Previous capacities

The course assumes advanced C++ and GPU progamming skills, and computer graphics.
Completing, for instance, FRRU an AM should provide enough background.