Credit: Anna Logue
Image Processing
Contents
In this course, we teach the fundamentals of image processing, starting from the human visual system and the basics of digital image acquisition. The goal is to understand the technical and theoretical basis of image processing and to be able to implement basic algorithms in practice.
Time and Location
- Lecture: Monday, 13:45, B6, 23–25 A101, Start: 12.02.2024
- Exercise: Tuesday, 15:30, Start: 20.02.2024
Instructor
Final mark
- Oral Exam
Participation
- The course is open to students of the Master Business Informatics, Master Business Mathematics and Mannheim Master in Data Science (MMDS).
- The course is restricted to 30 participants.
- Places are assigned on first come/
first serve basis. - Students register for the course by email to me (keuper@uni...). If you have sent me an email and NOT received a personal reply, this means that I reserved your seat!
Slides and Excercises
- provided in ILIAS
Requirements
- Basic Python skills are beneficial for the exercise.
- Basics in linear algebra are beneficial for the lecture.
Outline
Week
Topic
09.10.2023
General Remarks & Human Visual System
16.10.2023
Basics of Imaging – Image Enhancement
23.10.2023
Image Sampling
30.10.2023
Noise and Basic Image Operations
06.11.2023
Edge Detection and Scale Pyramid Models
13.11.2023
Energy Minimization + Variational Optimization (1)
20.11.2023 Variational Optimization (2) + Image Segmentation (1) (Level Sets) 27.11.2023 Image Segmentation (2) Superpixel Methods and Image Formats (remote session) 06.11.2023 Stereo Vision and Structure from Motion – plus Q&A session Literature
- R. Szeliski: Computer Vision Algorithms and Applications, Springer, 2010. ISBN: 978-1-84882-934-3. (Online available: http://szeliski.org/Book/)
- Forsyth, J. Ponce: Computer Vision: A Modern Approach, Prentice Hall, 2nd edition, 2012. ISBN: 978-0136085928 (Online available: cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf)