Overview DIP Methods

Digital Image Processing

Dates for the lecture and exercise see RWTHOnline

Type of examination: written exam

This lecture covers the fundamentals and applications of imaging and image processing, and illustrates them with numerous examples. Prerequisite is a completed Bachelor’s degree, e.g., in Electrical Engineering and Information Technology, Computer Engineering or Computer Science. Teaching materials are accessible via the teaching and learning portal Moodle of RWTH Aachen University:

https://www.elearning.rwth-aachen.de

Contents of the lecture

  1. Introduction to DIP, Geometric Optics and Image Sensors
  2. Digital Imaging Fundamentals (Digital Imaging Basics, Sampling and Quantization, Interpolation, Image Derivatives)
  3. Point Operations and Basic Neighborhood Operations
  4. Neighborhood Operations and Processing in the Frequency Domain
  5. Deep Learning Crash Course
  6. Edges, Lines and Corners
  7. Image Denoising and Deconvolution
  8. Image Registration
  9. Color Image Processing
  10. Morphological Image Processing
  11. Segmentation Basics
  12. Advanced Segmentation Methods
  13. Repetitorium and Questions

Literature

  • R.C. Gonzalez, R.E. Woods: Digital Image Processing, Global Edition, Pearson Education Limited, 4th edition, 2018.
  • J. Beyerer, et al., Machine Vision: Automated Visual Inspection: Theory, Practice and Applications, Springer, 2016.
  • B. Jähne: Digital Image Processing, 7th edition Springer, 2022.
  • M. Sonka et al.: Image Processing, Analysis and Machine Vision, 4th edition, Cengage Learning, 2014.