Computer Vision and Virtual Reality for Biomedical Ingeneering

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

The aim of this subject is to explain basic approaches used in analyses of

digital images. First, the student will learn about image capturing, its

digitization, image hardware, and procedures for processing two-dimensional

images. The methods for analysis spatial three-dimensional images will

follow. Model-based vision. Active vision. Industrial applications.

Practical recommendations. Computer vision as data source for virtual

reality.

Requirements:

Syllabus of lectures:

1. What are computer vision, concepts, and summary of relevant parts of ZSO

2. Different computer vision theories, namely Marr's one.

3. Relation to human vision, physiological and psychological considerations

4. Image segmentation, invariants and scale space.

5. Basics of projective geometry. Geometrical camera calibration.

6. Image capturing from radiometric point of view. Shape from shading.

7. Bilinear and trilinear relations among views.

8. Devices and techniques for capturing 3D images.

9. Stereo vision. Motion detection and analysis.

10. Analysis of 3D images. Bottom up approach. Shape from X.

11. 3D reconstruction, image reconstruction. Point clouds and surface.

12. Model-based vision. Active vision.

13. Industrial applications. Practical recommendations.

14. Computer vision as data source for virtual reality.

Syllabus of tutorials:

The exercises are conducted in the laboratory. Teams of two students solve,

defend and present two small projects. The aim is to introduce real

industrial topics to students. Projects are changed each semester. The

project consists of its specification, solution proposal, experiments,

realization, demonstration of functionality, and defense of a report. The

solution is presented to other students on a seminar.

1. Assignment of projects. Introduction to tools available for solving

projects.

2. Assignment 1, image-based industrial inspection using dedicated SW tool.

3. Assignment 1 - development in the laboratory.

4. Assignment 1 - development in the laboratory.

5. Assignment 1 - development in the laboratory.

6. Report. Defense of Assignment 1.

7. Assignment 2, more complicated vision task, development in Matlab.

8. Assignment 2 - development in the laboratory.

9. Assignment 2 - development in the laboratory.

10. Assignment 2 - development in the laboratory.

11. Assignment 2 - development in the laboratory.

12. Assignment 2 - development in the laboratory.

13. Report. Defense of Assignment 2.

14. Presentation of assignments to the whole class, credit.

Study Objective:

Study materials:

[1] Jain, R., Kasturi, R., Schunk, B. G.: Machine vision. McGraw-Hill, New

York, 1995

[2] Šonka, M., Hlaváč, V., Boyle, R.: Image processing, analysis and machine

vision. PWS Boston, 1999

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: