Module CS4250-KP04, CS4250
Computer Vision (CompVision)
Duration
1 Semester
Turnus of offer
each summer semester
Credit points
4
Course of studies, specific fields and terms:
- Master CLS 2023, optional subject, computer science
- Master MES 2020, optional subject, computer science / electrical engineering
- Master Computer Science 2019, optional subject, Elective
- Master Media Informatics 2020, optional subject, computer science
- Master Biophysics 2019, optional subject, Elective
- Master Biomedical Engineering, optional subject, advanced curriculum
- Master CLS 2016, optional subject, computer science
- Master MES 2014, optional subject, computer science / electrical engineering
- Master Media Informatics 2014, optional subject, computer science
- Master Computer Science 2012, optional subject, advanced curriculum imaging systems
- Master CLS 2010, compulsory, computational life science / imaging
- Master MES 2011, advanced curriculum, imaging systems, signal and image processing
- Master Computer Science 2012, optional subject, advanced curriculum signal and image processing
- Master Computer Science 2012, compulsory, specialization field robotics and automation
- Master Computer Science 2012, compulsory, specialization field bioinformatics
- Master Computer Science 2012, optional subject, advanced curriculum intelligent embedded systems
Classes and lectures:
- Computer Vision (exercise, 1 SWS)
- Computer Vision (lecture, 2 SWS)
Workload:
- 45 hours in-classroom work
- 55 hours private studies
- 20 hours exam preparation
Contents of teaching:
- Introduction to human and computer vision
- Sensors, cameras, optics and projections
- Image features: edges, intrinsic dimension, Hough transform, Fourier descriptors, snakes
- Range imaging and 3-D cameras
- Motion and optical flow
- Object recognition
- Example applications
Qualification-goals/Competencies:
- Students can understand the basics of computer vision.
- They can explain and perform camera choice and calibration.
- They can explain and apply the basic methods for feature extraction, motion estimation, and object recognition.
- They can indicate appropriate methods for different kinds of computer-vision applications.
Grading through:
- Oral examination
Responsible for this module:
Literature:
- Richard Szeliski : Computer Vision: Algorithms and Applications Springer, Boston, 2011
- David Forsyth and Jean Ponce : Computer Vision: A Modern Approach Prentice Hall, 2003
Language:
- English, except in case of only German-speaking participants
Notes:
Admission requirements for taking the module:- None
Admission requirements for participation in module examination(s):
- Regular participation in the exercises as specified at the beginning of the semester
- Successful completion of exercise slips as specified at the beginning of the semester
Module exam(s):
- CS4250-L1: Computer Vision, oral exam, 100% of module grade
Is identical to module XM2330 of the University of Applied Sciences Lübeck
Last Updated:
01.02.2022