Important information

  • An overview of the lecture in WS 2022/2023 is available  here.

  • The lecture is held in German.

  • All documents (lectures and exercises) will be provided electronically in Moodle. A printed lecture script is also available in the copy shop from the beginning of the semester.

  • If you are interested in the lecture, please register for it in Moodle! The registration is a prerequisite for participation! You will receive the password or automatically via eMail  or on request via eMail to rico.nestler@tu-ilmenau.de.

Responsible / Contact

Dr.-Ing. Rico Nestler

Target group and requirements

Students of the BA courses Biomedical Engineering as well as Computer and Systems Enineering and Computer Science, Media Technology and Electrical Engineering. MA/BA students with an interest in image processing as a supplement or to round off their studies.

Good knowledge of physics, mathematics and information or communications technology is helpful. As well lectures on technical optics, system theory, signals & systems.

Overview

Grundlagen der BildverarbeitungDr. R. Nestler

The lecture Basics of Image Processing and Pattern Recognition(Image Processing 1) deals with methods for solving recognition tasks with camera-based technical systems. Nowadays, camera-based ("seeing") technical systems are widely used in automation technology, robotics, medical technology, surveillance technology and in the automotive sector.

The course initially focuses on digital images with scalar pixel values (so-called grey-scale images), which have to be evaluated in terms of concrete tasks. The overall goal of this evaluation is the interpretation of the image content on different levels of abstraction. For this purpose, the images have to be processed, transformed, converted, analysed and finally classified in their technically accessible form in order to derive relevant contents and statements. In the course, essential methods, procedures and algorithms will be considered and discussed in the context of concrete applications from practice.

In addition to the purely informatic aspects of digital image processing, the lecture also teaches important contexts for the creation and description of digital images.

As a result, the student is able to understand and design classical processing chains for solving image-based recognition tasks, to correctly classify and implement partial aspects of processing solutions, and to move conceptually confidently in this interdisciplinary field of knowledge. For the methodical understanding of current application areas of Artificial Intelligence, such as Deep Learning, the best prerequisites are created.

The course is accompanied by an exercise, in which the lecture contents are reviewed, deepened and simple BV tasks are worked on with a prototyping software for image processing solutions (VIP-Toolkit). Numerous teaching examples are provided for the lecture.

Building on the contents taught in the lecture, the student can use the acquired knowledge in further courses of the bachelor and master studies, e.g.

as well as further courses on applied image processing / artificial intelligence at the TU Ilmenau.

 

Contents

Bildverbesserung mit DiffusionsfilternDr. R. Nestler
Instanzsegmentierung durch statistische HintergrundmodelleDr. R. Nestler

Introduction / Basics

  • Technical recognition processes / paradigms

  • Aspects of primary perception / digital imaging

  • Image representations and transformations

Basic methods of image preprocessing

  • Geometric image transformations

  • Image statistics and point operations

  • Linear and non-linear methods for image enhancement, edge detection

  • Morphological operations

Selected aspects of high-level image analysis

  • Image segmentation

  • Feature extraction and classification

Exercises with VIP Toolkit™-Rapid Prototyping Student Version.

References

  • J.Beyerer, F.P. Puente Leon, C. Frese: Automatic visual inspection - fundamentals, methods and practice of image acquisition and image evaluation. Springer Verlag 2012, ISBN 978-3-642-23965-6.

  • F. Wahl: Digital image signal processing, Springer Verlag 1989, ISBN 3-540-13586-3

  • W. Abmayr: Introduction to digital image processing. B.G. Teubner Stuttgart 1994, ISBN 3-519-06138-4

  • B. Jähne: Digital image processing and image retrieval. Springer; Edition: 7th, 2012, ISBN 978-3642049514.

  • B. Jähne: Digital image processing. Springer; 1994, ISBN 3-540-61379-X

  • P. Haberäcker: Praxis der Digitalen Bildverarbeitung und Mustererkennung. Hanser Reference Book, 1995, ISBN 978-3446155176

  • P. Haberäcker: Digital Image Processing. Hanser Reference Book, 1991, ISBN 978-3446163393

  • P. Haberäcker: Praxis der Digitalen Bildverarbeitung und Mustererkennung. Hanser Reference Book, 1995, ISBN 978-3446155176

Examination modalities

P achievements as a prerequisite for module completion through homeworks during the semester. Module grade from a written exam, 90 minutes or an oral exam (by arrangement).

For BA module "Computer Vision" of the degree courses IN and II: The module examination "Computer Vision" includes the exams "Grundlagen der Bildverarbeitung und Mustererkennung (BV 1)" and "Grundlagen der Farbbildverarbeitung (BV2)" . Please ask your examination office about registering for the exam!

Dates, scripts, teaching and practice materials

Dates, scripts, teaching & exercise materials can be found in Moodle. Here you get to the Moodle courses of the FG...