Courses in Summer Term

Here you can find an overview of our group’s courses given during Summer terms. You can click on each course to find out more detailed information about it.

Lectures

Mustererkennung

for B. Sc. and M. Sc. studies

This is a basic course on the field of pattern recognition, which is the best starting point for your endeavour in the field of computer vision. It covers a broad field of topics ranging from discretizing real world data in a proper way, over data manipulation and transformation techniques to supervised and unsupervised learning concepts. All topics are introduced in a rather formal and thus very mathematical way and are applied to image and language processing tasks. Before you attend this course you should have finished most of the mathematics basic courses (Linear Algebra, Calculus, Numerics, Stochastic) of the B. Sc. Computer Science study program.

Rechnersehen 2

for M. Sc. studies

This course teaches the basics of 3-D computer vision. The first part of the course covers camera models, the process of image acquisition and typical problems (like image noise), that need to be tackled to process real world images. Furthermore, there is an introduction to projective geometry. In the second part of the course methods for extraction of features based on single images and series of images are introduced. These are important for the following tasks like 3-D reconstruction of objects and scenes, as well as object recognition in 3-D. In the third part of the course, we look into methods that allow us to calibrate a camera system, i.e. estimating camera parameters automatically. This part also covers techniques that use a series of images to retrieve such parameters and estimate depth information. The final part of the course covers methods for modelling and estimating movement in image sequences, as well as ways to achieve 3-D object recognition. This course is accompanied by a practical exercise. We highly recommend to finish course Rechnersehen 1 before attending this course.

Visuelle Objekterkennung

for B. Sc. and M. Sc. studies

This is a computer vision course with a focus on the tasks of visual object recognition using machine learning techniques. It is a complemental course for Mustererkennung and Rechnersehen 1 and focuses on more modern approaches for classification and segmentation in images. It also covers some formal basics regarding object recognition and localisation as task. You do not need any prior knowledge in image processing and computer vision for this course. However, we recommend to attend this course after finishing Rechnersehen 1 and/or Mustererkennung.

Image Processing (M.Sc. Photonics)

for M. Sc. studies

This lecture will be given for master students of the Abbe School of Photonics. For details about other lectures in this master course visit the course and schedule plan of the school. The overall goal of this lecture is to impart knowledge about image processing and computer vision and related algorithms. This course is accompanied by a practical exercise.

Spezielle Probleme im Rechnersehen

for M. Sc. studies

This is a very research-ortiented course for topics in the field of computer vision. Contents of this course highly vary and cover most recent research of the Computer Vision Group Jena. Students, PhD students and PostDocs of the group present their own research topics in talks, which are then discussed. As a prerequisite for admission in this course, you have to do a project work, Bachelor thesis or Master thesis in our group at the same time. Regular attendance is required, as well.

Informatik I (B.Sc. Physik)

for B. Sc. studies

This is an entry level course on computer science for students of B.Sc. Physics. Given a pre-selected programming language (e.g. C++ or Python), this course covers mainly basics in programming. Additionally, there are chapters on basic computer science knowledge like algorithms, data structures, recursion and complexity. This lecture is accompanied by practical programming exercise.

Programmieren in C++ (B.Sc. Mathematik)

for B. Sc. studies

This is an entry level course on programming in the programming language C++ for students of B.Sc. Mathematics. It covers basic knowledge on programming concepts like datatypes, control structures, functions, arrays and basic I/O. This lecture is accompanied by practical programming exercise.

Objektorientierte Programmierung mit C++ (ASQ)

for B. Sc. studies

This is an entry level course on object-oriented programming in the programming language C++. The first part covers basic knowledge on programming concepts like datatypes, control structures, functions, arrays and basic I/O. In the second part, the concept of object-oriented programming is introduced and how to implement it in C++. This lecture is accompanied by practical programming exercise.

Seminars

Rechnersehen / Fortgeschrittene Methoden im Rechnersehen

for B. Sc. and M. Sc. studies

This course covers advanced methods and techniques in computer vision in greater depth. Topics are not fixed, but rather align with recent state-of-the-art literature in various areas of computer vision research. Typical areas for topics are deep learning, supervised and unsupervised learning, reinforcement learning, 3D vision, as well as application works in e.g. biodiversity research, environmental science, medical research or autonomous driving. In seminars, students will have to work on a certain topic on their own. At the end of the term they need to submit a term paper (essay) and give an oral presentation of their work.

Labs

Anwendungspraktikum 3-D Rechnersehen / Projekt “Intelligente Systeme”

for B. Sc. and M. Sc. studies

In this course students work independently in small teams on specific computer vision software projects. The goal is to familiarize yourself with image processing and machine learning algorithms in a hands-on manner. Students can propose own projects, but there is also always a set of pre-defined topics to choose from. The course is usually accompanied by some small lectures on tools (e.g. Git, Docker, Python, etc.) and scientific methods. At the end of the project, students need to submit a documentation of their work. During the term, there are milestone meetings with the supervisor. We highly recommend to finish at least courses Rechnersehen 1 and/or Rechnersehen 2 before attending this course.