Data Visualisation (FSI-SVD)

Academic year 2018/2019
Supervisor: doc. PaedDr. Dalibor Martišek, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
Students will be made familiar with basic methods of 3D data reconstruction and conditions for their use.
Learning outcomes and competences:
Students will be able to visualise the common types of 3D data that are not suitable for tabulation.
Prerequisites:
Students are expected to be familiar with basic programming techniques and their implementation in Borland Delphi, and with basic 2D and 3D graphic algorithms (colour systems, projection, curves and surfaces construction)
Course contents:
The course is lectured in winter semester in the fourth year of mathematical engineering study. It familiarises students with basic principles of basic algorithm of computer modelling of 2D and 3D data, namely of scalar fields. Lecture summary: Construction of implicit curves and surfaces, contour lines and iso-surfaces. Algorithms, which construct surfaces – marching cubes and volume algorithms - ray casting, ray tracing.
Teaching methods and criteria:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Graded course-unit credit is awarded on condition of having worked out semester work
Controlled participation in lessons:
Missed lessons may be compensated for via a written test.
Type of course unit:
    Lecture  13 × 1 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture 1) Curves defined by equation f(x,y)=0, surfaces defined by equation f(x,y,z)=0 – pixel algorithm
2) Curves defined by equation f(x,y)=0 – grid algorithm
3) Surfaces defined by equation f(x,y,z)=0 – marching cubes algorithm
4) Contour lines of surface
5) Surface visualisation using the palette
6) 2D visualisation of 3D data grid
7) 3D visualisation of 3D data grid using marching cubes algorithm
8) 3D filters
9) 3D visualisation using volume methods – ray casting.
10) 2D reconstruction of confocal microscope outputs
11) 3D reconstruction of confocal microscope outputs
12) 2D reconstruction of Visible Human Project data
13) 3D reconstruction of Visible Human Project data
    Computer-assisted exercise 1) Curves defined by equation f(x,y)=0 – pixel algorithm
2) Surfaces defined by equation f(x,y,z)=0 – pixel algorithm
3 Curves defined by equation f(x,y)=0 – grid algorithm
4) Surfaces defined by equation f(x,y,z)=0 – marching cubes algorithm
6) Contour lines of surface, surface visualisation using the palette
7) 2D visualisation of 3D data grid
8) 3D visualisation of 3D data grid using marching cubes algorithm, 3D filters
9) 3D visualisation using volume methods – ray casting.
10) 2D and 3D reconstruction of confocal microscope outputs
11,12) 2D and 3D reconstruction of Visible Human Project data
13.14. Semester work processing.
Presence in the seminar is obligatory.
Literature - fundamental:
2. Martišek, K.: Adaptive filters for 2-D and 3-D Digital Images Processing, FME BUT Brno, 2012
Literature - recommended:
1. Martišek, D.: Matematické principy grafických systémů, Littera, Brno 2002
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2A-P full-time study M-MAI Mathematical Engineering -- GCr 4 Compulsory 2 2 S