A. Course Introduction, The four Rs of computer vision
B. Cameras and Lenses, Camera calibration, View geometry and homograpy
C. Image Filtering, Hough transformation
D. Image Representations: pyramids
E. Textures and patterns
F. Color understanding by computer, color models and representations
G. Global and Local Features
H. Multiview Geometry
I. Affine Transformations and Reconstructions
J. Projective Geometry
K. Model Based Recognition
L. Segmentation and Clustering, Segmentation and Fitting
M. Shape modelling and rendering
N. Tracking
O. Example based methods in CV: face recognition, medical imaging, UAV navigation

understand the typical steps for solution of image processing/vision problems: pre-processing, segmentation,
description, and recognition

possess knowledge and understanding of basic and some advanced methods for each step in the
process

be able to choose appropriate methods and implement solutions to small-scale image processing and
vision problems

The students will be aware of different application areas in computer vision, and the underlying mechanisms

R. Szeliski, Computer Vision: Algorithms and Applications, Springer , ISBN: 9781848829343

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press , ISBN: 9780521540513

R. Gonzalez and R. Woods, Digital Image Processing, Prentice-Hall , ISBN: 013168728X

R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Wiley-Interscience Publisher , ISBN: 9780471056690

Computer Vision and Image Understanding, Elsevier

International Journal of Computer Vision, Springer