Digital Image Processing and Introduction into Pattern recognition

Module Code
Module Coordinators
Klaus Rinn
Klaus Rinn
Short Description
Cameras and illumination,image transformations, advanced image filters, segmentation, morphological filters. Statistical pattern recognition: regions, descriptors, classifiers.
Learning Objectives

Based on the knowledge of the most important principles and common methods of image processing and pattern recognition, graduates are able to evaluate the applicability of new methods in practice critically. By using the acquired methods they are able to develop solutions of the most important tasks of image processing, in which the interdependence of hard - and software calculation is utilized.

  • Hardware: cameras and illumination
  • Image transformations
  • Advanced image filters
  • Template matching
  • Segmentation
  • Morphological filters
  • Introduction to statistical probability theory: regions, descriptors, classifiers
Duration in Semester
Instruction Language
Total Effort
6 CrP; an estimated 180 hours, of which approximately 60 are spent in class.
Weekly School Hours
Method of Instruction

Lecture 3 SWS, Exercises 1 SWS

Requirements for the awarding of Credit Points

Examination prerequisite: Lab exercises

Examination: Written or oral exam (The form of the examination will be announced to the students in a timely and appropriate manner)

Evaluation Standard
according to examination regulations (§ 9)
  • R. C. Gonzalez, R.E. Woods: Digital Image Processing Prentice Hall
  • Burger, Burge: Digitale Bildverarbeitung - Eine Einführung mit Java und ImageJ Springer
  • Duda, Hart, Stork: Pattern Classification Wiley-Interscience