On this page, you will find all relevant information regarding your Image Analysis and Computer Vision course, taught by prof. Luc Van Gool, prof. Ender Konukoglou, and prof. Orcun Goksel.


Content The first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.


Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.


Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and Python. The course language is English.


The lectures will take place in room ETF C1 on Thursdays in the Autumn Semester from 13:00 to 16:00. Then, from 16:00 to 17:00, the practical exercise sessions will take place in rooms ETZ D61.1 and ETZ D61.2. TAs will stay until 18:00 to answer your questions. The exercises this year will be implemented in Python.

Handing in solutions

All your exercises have a theoretical and a practical part. Assistants have been instructed to discuss the theoretical bit verbally with you. You will then be asked to give a short demo of your working solutions to the practical bit. You need to provide a satisfactory answer to each theoretical question AND demonstrate that your practical solution works as expected otherwise your submission will be deemed incomplete. To make sure that the submission process runs as efficiently as possible, only attempt to submit once you have everything in working order.


The script is available for purchase after the beginning of the exercise session for CHF 10.00 in room ETF C112. There are enough copies to go around and more may be ordered if needed.

Lecture Slides

Here are the lecture slides for the Image Analysis and Computer Vision course (will be updated during the semester).

Week #



HS17: Introduction


HS17: Digital Image Formation (Recording & display)


HS17: Sampling and Quantisation


HS17: Image Enhancement and Feature Detection


HS17: Unitary Transforms


HS17: Segmentation


HS17: Color and Texture


HS17: Deformable Shape Matching


HS17: Motion Extraction


HS17: Feature Extraction


HS17: 3D Data Extraction


HS17: Tracking


HS17: Specific Object Recognition


HS17: Object Category Recognition


Slides from previous years


Here is the exercise schedule for the Image Analysis and Computer Vision course. In the first week of the course exercise session, you will be given an introductory task called Exercise 0. Thereafter, you will be provided with the other exercise sheet. The week after the deadline of each exercise, you will be handed the next one. The solution code and a solution sheet for each exercise will be posted here after the deadline for that exercise has passed.

In addition, the exercises will be linked, as described here, by a pipeline.


Exercise Sheet and Required Material




Introduction to Python (Material)




Image Acquisition (Material)


Theoretical, Practical


Feature Extraction (Material)


Theoretical, Practical


Stereo Vision (Material)


Theoretical, Practical

Connecting via SSH

You might need to follow the following instructions in order to log in to your student accounts for the course using ssh from an external device:

1) Please connect to your VPN. Any attempt to connect from outside the ETH domain is automatically rejected.

2) The host name has the following general format: OR where XX can be any number between 01 and 37 inclusive and YY can be any number between 01 and 40 inclusive.
For example, a working hostname is:

3) Port to connect to: 22

4) Enable X11 forwarding to be able to display images.

5) Use the username and password you were provided at the beginning of the exercise sessions.

6) Command: /usr/bin/xterm -ls

Recommended software for Windows users: Cygwin, X-Win32, X-Deep/32 or PuTTY.

In case you want to work from your local machines (laptop), it is easy to install Anaconda Python and Launch Jupyter-Notebook from there.

If, however, you prefer working on your course account, enable port forwarding on your ssh request. Here what works (tested on Cygwin, with port=5000):
ssh -L5000:

jupyter-notebook --port=5000
Open the browser of your local machine, and paste the given link.

Responsible Assistants

If you have any questions, please feel free to email the responsible assistants for the exercise that your questions pertain to. Here is the list of assistants who will be helping you out with the exercises.



Exercise 0

Nikolay, Sergi

Exercise 1

Yuhua, Christos, Jiqing, Neerav, Xiaoran

Exercise 2

Yuhua, Christos, Arun, Simon, Xiaoran

Exercise 3

Kevis, Arun, Jiqing, Neerav, Simon

Exam instructions

The IACV examinations are structured as follows. You will have the opportunity to come to the preparation room - usually room ETFC 109 - an hour before your scheduled exam time. During that period, you will be given the exam questions to prepare but you will not be allowed to use any aid other than pen and paper. Then, you will be sent to the exam room - usually room ETFC 117 - for your oral test comprising 2 sessions, each lasting 15 minutes.

As to the material that has to be studied for the exam of HS17, you should use the slide decks made available to you for this year's lectures as the reference. The material in there has to be studied. The course text can be used in as far as you find it useful to further explain what is in the slides. Material in the course text that is not covered by the slides does not have to be studied.

Example exam questions can be found here.

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