Learning Outcomes
Image acquisition. Mathematical modeling of image formation. Inntroduction to image
processing and analysis. Camera calibration. Stereo vision. Depth estimation. Object localization.
3D image analysis. Surface geometry. Object topology. Object landmarks and features. Object
recognition. Object registration. Object description. Applications in medical imaging, image
retrieval, robotic vision.
Course Content (Syllabus)
Image acquisition. Mathematical modeling of image formation. Inntroduction to image
processing and analysis. Camera calibration. Stereo vision. Depth estimation. Object localization.
3D image analysis. Surface geometry. Object topology. Object landmarks and features. Object
recognition. Object registration. Object description. Applications in medical imaging, image
retrieval, robotic vision.
Additional bibliography for study
N. Nikolaidis and I. Pitas, 3D Image Processing Algorithms, J. Wiley, 2000.
R.Szelinski, “ Computer Vision ” , Springer 2011
Hartley R, Zisserman A. , “ Multiple view geometry in computer vision” . Cambridge university press; 2003.
Davies, E. Roy. “Computer vision: principles, algorithms, applications, learning ”. Academic Press, 2017
Trucco, Emanuele, and Alessandro Verri. Introductory techniques for 3-D computer vision. Vol.201. Englewood Cliffs: Prentice Hall, 1998.