Study Program: Electrical Engineering, 2nd Bologna Cycle
Semester: winter semester
The aims of this course are to introduce basic concepts, underlying theory, algorithms, and applications of computer vision.
The aims of computer vision, the origins of computer vision, and related fields.
Computer vision trends and application domains.
- Image formation
Perspective projection camera model.
Camera calibration, direct linear transform, lens distortion correction.
Propagation of light, photometry, photometric lens equation.
Cameras and lenses, lighting techniques.
Human eye, color perception, reproducing color, color spaces.
- Image analysis
Image filtering, histogramming.
Edge detection, corner detection.
Connected components analysis.
Active contour models (snakes).
Scale space and image pyramids.
Geometric image transformations, similarity measures.
Image registration, model fitting, RANSAC.
- Stereo vision
Basic concepts of stereo vision.
Modeling and calibration, epipolar geometry.
Active stereo, structured lighting.
- Visual motion analysis
Time to collision.
Optic flow, motion field, velocity field.
Visual tracking, basic Kalman filtering.
- D. Forsyth, J. Ponce, Compuer vision, a modern approach, Prentice Hall, 2003.
- E. Trucco, A. Verri, Introductory techniques for 3-D computer vision, Prentice Hall, 1998.
- M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis and machine vision, Chapman and Hall Computing series, 1993.
Basic knowledge of applied mathematics (vectors and matrices, linear algebra) and physics (optics).