Techn. Fakultät Website deprecated and outdated. Click here for the new site. FAU-Logo

Computer Vision [CV]


Dates & Rooms:
Monday, 14:00 - 15:30; Room: 09.150
Tuesday, 12:00 - 12:45; Room: 09.150


The exams will be on the following dates:

Tuesday      02. August 2011
Tuesday      27. September 2011
Wednesday 28. September 2011

To register for an exam either email one of the secrataries, Kristina Müller or Iris Koppe , or personally visit the secretaries office at 09.138.


There will be two extra 45-min. lectures on the following dates and times:
Monday, 20. June 2011, 12-12:45pm
Monday, 27. June 2011, 12-12:45pm

Course Description

Computer Vision involves the (semi-) automatic extraction of
information from images. The image data itself can take many forms:
color or black-and-white images, video sequences, multiple cameras,
data from medical scanners, etc. The information that should be
extracted can also vary depending on the application: locating an
object in an image (image database search), precisely measuring the
dimensions of an object (quality control), following a moving item
(surveillance), identifying letters and numbers (optical character
recognition), estimating the position  and orientation of a specific
object (robot arm guidance), etc. As a result, the field of computer
vision covers a wide variety of topics, which may sometimes, at first
glance, seem unrelated.

This course provides an introduction to the field of Computer Vision,
focusing on the underlying algorithmic, geometric and optic issues. It
starts with a description of image formation, including geometric,
optic and electronic aspects of the image formation process. Lower
level algorithms are then presented on the extraction of different
types of image features (edge detection, texture, color,
multi-resolution analysis, Hough transform, deformable contours). The
course will also cover topics associated with extracting information
from multiple images (stereo, motion). The last set of topics will
cover higher level analysis like grouping, and classification with
examples on image retrieval and face detection.

Recommended Literature

  • E. Trucco, A. Verri. <b>Introductory Techniques for 3-D Computer Vision</b>. <br> Prentice Hall 1998. ISBN: 0-13-261108-2H <br> &nbsp;
  • D. A. Forsyth, J. Ponce. <b>Computer Vision: a Modern Approach</b>. <br> Prentice Hall 2002. ISBN: 0-13-085198-1

Lecture Plan

The schedule of the lecture is still subject to change.

The course schedule has been updated to more accurately reflect what has been covered so far in the lectures. The update version can be found in this PDF.


The slides will become available as soon as possible, but usually shortly after the lecture. 

Slides from last year can be found here.

Introduction:A brief introduction of the various topics of computer vision, course motivation and guidelines.
Image Formation:Lens, radiometry, geometric optics, coordinate systems, projection.
Cameras:Digital image capture: from image irradiance to pixel values.
Smoothing:Sensor noise and methods for reducing image noise.
Edge Detection:Gradient-based edge detection, Canny edge detector, Laplacian of Gaussian, Gaussian pyramid, Laplacian pyramid.

Texture recognition, oriented filters, texture synthesis, shape from texture.

ColorThe physics of color, trichromacy, color perception, color spaces, example applications.
Hough Transform:Line detection, circle detection, ellipse detection, HT for arbitrary shapes.
Binocular stereo:Basic binocular stereo setup, disparity, triangulation, correspondence problem
Structured Light:Structured light setup, triangulation, binary coding
Multiview Geometry:Epipolar geometry, epipolar constraint, eight-point algorithm
Deformable Contours:Active contours, energy functional, greedy minimization
Motion:Background subtraction, optic flow, motion field, optic flow computation
Kalman Filtering:Predictive motion analysis, dynamic system under observation, Kalman filter formulation, extended Kalman filter.
Particle Filters:Markovian synamic systems, Bayesian estimation, particle filters, marginalized particle filters.  
SIFT Features:Scale Invariant Feature Transform, keypoint detector, SIFT feature vector construction, matching SIFT vectors.




The exercise page for this year is located here.

Exercises from last year can be found here.