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

  • Imanol Muñoz Pandiella ( )

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 Visualization. Perception in Visualization
    Basic concepts of visualization: goals, tasks, users.
    Elements of perception and its application in Visualization: pre-attentive variables, visual channels...
  2. Multi-dimensional data visualization
    Techniques for visualization of multiple-dimensional data.
  3. Multiple Views Visualization
    Multiple Views. Common designs, examples, analysis of advantages and inconvenients.
  4. Molecular visualization
    Introduction to Molecular Visualization: motivation, data, and rendering algorithms.
  5. GPU-based Volume Rendering
    Presentation of the main algorithms of direct volume rendering, including 3D textures and ray-casting. Transfer fuctions. GPU-based ray-casting.
  6. Advanced Scientific Visualization Techniques
    Introduction to Molecular Visualization: motivation, data, and rendering algorithms.
    Introduction to DTI rendering: data, applications, measures, algorithms.

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:

Web links

Previous capacities

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