Course: Graphic Systems and Data Visualization

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Course title Graphic Systems and Data Visualization
Course code KIV/GSVD
Organizational form of instruction Lecture + Tutorial
Level of course Bachelor
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
  • Skala Václav, Prof. Ing. CSc.
Course content
1 - introduction, semester organization. The task of data visualization, the paradigm of data structures 2 - The paradigm of visualization tools, introduction to vtk, paraview and MVE 3-4 - visualization techniques: scalar data visualization - color/visibility mapping, slicing, glyphs, vector data visualization - flowlines, streamlines, hedgehog 5-6 - visualization techniques: volume data, direct rendering, iso-suface extraction, probing, cutting 8 - introduction to MVE: maps, modules, links, running a map 9-10 - advanced MVE: module libraries, submaps, events, adding a new module 10 - data preprocessing methods for visualization 11-12 - presentation of course works 13 - invited talk

Learning activities and teaching methods
Lecture with practical applications, Skills demonstration
  • Contact hours - 26 hours per semester
  • Practical training (number of hours) - 26 hours per semester
  • Undergraduate study programme term essay (20-40) - 62 hours per semester
  • Presentation preparation (report) (1-10) - 6 hours per semester
  • Preparation for an examination (30-60) - 10 hours per semester
professional knowledge
Recommended prior knowledge of computer graphics at the level of a basic course (KIV/ZPG): geometry transformation, basic rendering methods, basics of color theory. Recommended prior knowledge of basic procedural and object-oriented programming.
learning outcomes
After taking the course the student should be able to - recall existing visualization methods and visualization tools - understand the general paradigm of visualization tools - classify data kinds with respect to their visualization and pick a proper visualization technique - visually analyze a broad spectrum of datasets - decide whether it is necessary to extend a particular tool in order to be able to perform a particular visualization - implement new visualization techniques and include them into existing visualization toolkits.
teaching methods
Skills demonstration
Lecture with practical applications
assessment methods
Combined exam
Seminar work
Individual presentation at a seminar
Quality of a written report
Recommended literature
  • VTK Textbook. Kitware, ????.

Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester