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227-0447-00L 6 Credits BSC , DR , MSC , NDS D-HEST , D-MAVT , D-INFK , D-PHYS , D-MATH , D-ERDW , D-ITET , D-BSSE
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Image Analysis and Computer Vision

Lecturers: Prof. Dr. Ender Konukoglu, Dr. Fisher Yu, Dr. Ertunc Erdil
Examiners: Prof. Dr. Ender Konukoglu, Dr. Fisher Yu
VVZ CR 2.95

Last Updated: 2026-02-05 16:30:26

Abstract

Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.

Objective

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

Content

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.

Resources

Lecture Notes

Course material Script, computer demonstrations, exercises and problem solutions

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC , NDS
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
No written aids are allowed in the exam.
Different parts of the lecture will be assessed in a maximum 2 hours written exam in English.Students need to complete at least 3 out of 6 assignments to be admitted to the final exam. Students who have completed less than 3 assignments, must deregister from the final exam.Students who complete all 6 assignments, will get a 0.25 bonus grade that will be added to the grade they receive at the final exam.All the assignments will be announced in the 2nd week of the semester, and students will have until the end of the semester to complete the assignments.Assignments will be of pass / fail type.Please note, the following is valid for all doctoral students that are enrolled under the old ordinance on the Doctorate at ETH:Doctoral students who participate at the course to earn ECTS points will receive a “Testat” without taking the written examination if their department rules allow this and provided they successfully complete all 6 exercises (6 ECTS). All other students must take the written examination.

Course Components

Type Title Time & Place Hours
lecture Image Analysis and Computer Vision
  • Thu 14:15-17:00 (HG F 1)
3 h weekly
exercise Image Analysis and Computer Vision
  • Thu 17:15-19:00 (ETZ D 61.1)
  • Thu 17:15-19:00 (ETZ D 61.2)
  • Thu 17:15-19:00 (ETZ H 91)
1 h weekly

Offered In