Computer Vision and Deep Learning 2019/2020

Lecture /w Excercise

Tuesday, 1-3pm; Thursday, 11am-1pm G29-335

Prof. Dr.-Ing. Klaus Tönnies, Johannes Steffen

An introduction to image processing:

W. Burger & M.J. Burge. Digital Image Processing – An Algorithmic Introduction Using JAVA. 2nd edition, Springer, 2016

Computer Vision deals with the extraction of information from images or image sequences comprising, e.g., object detection and classification, pose estimation, activity recognition, semantic segmentation and many others. It has found numerous applications in industry and research. Solutions for most of these computer vision tasks have been established many years ago but are nowadays carried out using deep neural networks. Within this course, we will briefly review important aspects of classical, mostly high level computer vision tasks and then explore the different network topologies that have been developed for end-to-end learning solutions. A background in basic image processing or computer vision is expected.

Lecture topics:

  1. Introduction
  2. Features from Images
  3. Working with Features
  4. Classification with Generative Models
  5. Classification with Discriminative Models
  6. Using Neural Networks
  7. Deep Convolutional Neural Networks
  8. Learning in Deep CNNs
  9. Object Detection and Deep Learning
  10. Semantic Segmentation and Deep Learning
  11. Other Computer Vision Tasks (Pose Estimation, Activity Recognition, Object Tracking)
  12. Generative Models and Deep Learning

Courses of study:

See LSF.


This course is limited to 25 students only.

To be allowed to participate in the course, students have to successfully pass the admission exam. Details will be announced soon.

Credits: 6

Language: Englisch

Accompanying project:

To be allowed to take part in the examination, students have to successfully participate in an accompanying project. Details will be discussed within the first lecture.