[ 921PECOCOVV20 ] VL (*)Computer Vision

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Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Oliver Bimber 2 SSt Johannes Kepler Universität Linz
Quellcurriculum Masterstudium Computer Science 2021W
Ziele (*)While Computer Graphics focusses on image synthesis, Computer Vision is all about image analysis and image understanding. It finds many applications in domains such as, 3D reconstruction, robotics, medical engineering, media technology, automatization, biometry, human-computer-interaction, contact free measurement, remote sensing, quality control, etc. This lecture will give first insights into the basics of Computer Vision. At the end of the semester, participants of this class will be able to apply and implement computer vision methods independently. A basic understanding of programming concepts is required. Detailed knowledge in a programming language, however, is not necessary. The associated lab will provide a sufficient introduction into Matlab, Matlab toolboxes, and hands-on computer vision techniques to prepare students for their team projects.
Lehrinhalte (*)Spatial and frequency domain processing, gradient domain processing, segmentation and object recognition, basics of cameras, geometric camera calibration, the geometry of multiple views, stereoscopic depth estimation, range scanning and data processing, structure from motion, computational photography.
Beurteilungskriterien (*)eExam (Moodle Test)
Lehrmethoden (*)Slide presentation
Abhaltungssprache Englisch
Literatur (*)
  1. Computer Vision – A Modern Approach, Forsyth and Ponce, 2nd edition, Addison Wesley, ISBN-10: 013608592X, 2011
  2. Multiple View Geometry in Computer Vision, Hartley and Zisserman, 2nd edition, Cambridge Press, ISBN: 0521540518, 2003
  3. Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, ISBN: 1848829345, 2010
  4. Image Processing: The Fundamentals, Maria Petrou and Costas Petrou, Wiley, 2nd edition, ISBN-10: 047074586X, 2010
  5. Learning OpenCV: Computer Vision with the OpenCV Library, Gary Bradski, Adrian Kaehler, Mike Loukides, Robert Romano, O'Reilly Media, ISBN: 9780596516130, 2008
  6. Handbook of Mathematical Models in Computer Vision, Nikos Paragios and Yunmei Chen, Springer, ISBN-10: 0387263713, 2005
  7. Machine Vision. Theory , Algorithms, Practicalities: Theory, Algorithms, Practicalities: Theory , Algorithms, Practicalities, E. R. Davies, Academic Press, 3rd edition, ISBN: 978012206093, 2005
  8. Computational Vision: Information Processing in Perception and Visual Behavior, Hanspeter A. Mallot, MIT Press, ISBN: 9780262133814, 2000
  9. Three-Dimensional Computer Vision – A Geometric Approach, Olivier Faugeras, MIT, Press, ISBN: 0262061589, 1993
Lehrinhalte wechselnd? Nein
Äquivalenzen (*)in collaboration with 921PECOCOVU20: UE Computer Vision (1.5 ECTS) equivalent to
921PECOCOVK13: KV Computer Vision (4.5 ECTS)
Teilungsziffer -
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