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 Konukoglu, and Prof. Orcun Goksel.


This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with 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 the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, 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 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.


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 Python and Linux. 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 programming exercises 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 solution will be deemed incomplete. To make sure that the presentation process runs as efficiently as possible, only attempt to present once you have everything in working order.


The script will be available for purchase on site during the first exercise session, and afterwards in room ETF D113.1, for CHF 20.00. 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 #



HS18: Introduction


HS18: Digital Image Formation (sampling & quantization)


HS18: Feature Extraction


HS18: Image Enhancement and Feature Detection


HS18: Unitary Transforms


HS18: Color & Texture


HS18: Segmentation


HS18: Optical Flow and 3D


HS18: 3D (same as week #8 - 2nd part)


HS18: Traditional Object Recognition


HS18: Deep Learning I (ppt version)


HS18: Tracking


HS18: Deep Learning II (ppt version)


HS18: Deep Learning III (ppt version)


Slides from previous years


Here is the exercise schedule for the Image Analysis and Computer Vision course. In the first exercise session on 20.09.2018, you will be guided through an introductory task called Exercise 0. Thereafter, you will be provided with the regular exercises handouts. The week after the deadline of each exercise, the handout of the next exercise will be posted. The solution code and a solution sheet for each exercise will be posted after the respective deadline has passed.

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


Exercise Sheet and Required Material




Introduction to Python (Material)




Basic Image Processing (Material)


Theoretical, Practical


Stereo Vision (Material)


Theoretical, Practical


Image Classification (Material) (Extra material) (PyTorch intro)


Theoretical, Practical

Extra material: example images that are used as input for the programming parts of the exercises.

Working Remotely

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 own 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


Exercise 1

Arun, Christos, Neerav, Xiaoran

Exercise 2

Arun, Martin, Simon, Xiaoran

Exercise 3

Anton, Christos, 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 HS18, 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 script 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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