Study Program: Electrical Engineering, 2nd Bologna Cycle
Semester: winter semester
Content (Syllabus outline):
- Multi-camera systems, multi-camera calibration, structure from motion, active vision.
- Feature detectors and descriptors, corner detectors, SIFT, HOG, MSER, COV, and others.
- Multi-resolution, multi-scale approaches.
- Deformable models, active contour models, active shape models, active appearance models.
- Image matching and registration, similarity measures, registration models.
- Object detection and tracking, tracking by detection, Kalman filters, particle filters.
- Deep learning.
- Computer vision and machine vision applications.
Objectives and competences:
The aims of this course are to cover selected topics of computer vision and to prepare students for team and independent research and development work.
Intended learning outcomes:
Be able to implement advanced computer vision algorithms. Be able to provide solutions to moderately complex problems.
Learning and teaching methods:
Lectures, laboratory work, home work, project.
- D. Forsyth, J. Ponce, Computer vision, a modern approach, Prentice Hall, 2003.
- R. Gonzales, R. Woods, Digital image processing, 2nd Ed., Prentice Hall, 2002.
- 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.
- A. Bovik (Ed.), Handbook of image and video processing, 2nd ed., Elsevier AP, 2005.