Subject description - BE4M39VIZ

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BE4M39VIZ Visualization Extent of teaching:2P+2C
Guarantors:Čmolík L. Roles:PO Language of
teaching:
EN
Teachers:Čmolík L., Slavík P. Completion:Z,ZK
Responsible Department:13139 Credits:6 Semester:L

Anotation:

In this course, you will get the knowledge of theoretical background for visualization and the application of visualization in real-world examples. The visualization methods are aimed at exploiting both the full power of computer technologies and the characteristics (and limits) of human perception. Well-chosen visualization methods can help to reveal hidden dependencies in the data that are not evident at the first glance. This in turn enables a more precise analysis of the data, or provides a deeper insight into the core of the particular problem represented by the data.

Course outlines:

1. Motivation for data visualization, history, categories of visualization 9infovis, scivis, software visualization,..)
2. Visualization of scalar data (visualization pipeline, data reduction)
3. Visualization of vector data (problems of visualization in 2D, 3D,..)
4. Visualization of volume data (marching cube, cuberille)
5. Visualization of volume data (volume data rendering, topological problems of volume data rendering,..)
6. Visualization of dynamic data (animation, time scale,..)
7. Information visualization (HomeFinder, TreeMaps, hyperbolic geometry)
8. Perception and interpretation of visualized data (context, human perception, psychology of perception)
9. Simulation and visualization (e.g. simulation and visualization of technological processes)
10. Visualization of medical data (tomography. Operation planning)
11. Technical illustration, medical illustration
12. Software visualization (visualization of software behavior, visualization of software maintenance ,..)
13. Problems of visual data mining. Applications of visual data mining (relation to neural computing)
14. Reserve

Exercises outline:

1. Semestral project assignement
2. Semestral project assignement
3. Consultations to semestral project
4. Consultations to semestral project
5. Consultations to semestral project
6. Consultations to semestral project
7. Checkpoint of semestral project
8. Consultations to semestral project
9. Consultations to semestral project
10. Consultations to semestral project
11. Consultations to semestral project
12. Semestral project presentation
13. Semestral project presentation
14. Semestral project assessment

Literature:

1. Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2002
2. Stasko,J., Domingue,J., Brown,M.H., Price, B.A.: Software Visualization, MIT Press, 1998
3. Chen, Ch.: Information Visualization and Virtual Environments,Springer, 1999

Requirements:

Webpage:

https://moodle.fel.cvut.cz/course/B4M39VIZ

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEOI9_2016 Data Science PO 2
MEOI1_2018 Human-Computer Interaction PO 2
MEOI3_2018 Computer Graphics PO 2
MEOI3_2016 Computer Graphics PO 2
MEOI1_2016 Human-Computer Interaction PO 2
MEOI9_2018 Data Science PO 2


Page updated 13.12.2019 17:52:09, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)