Inhalt

[ INMAWKVDBVA ] KV Digital Image Processing

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Josef Scharinger 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2025W
Learning Outcomes
Competences
Students are able to understand, apply, analyze, and assess conventional and AI methods that are needed to enhance images, automatically analyze image, compress images, and protect copyrights on digital images.
Skills Knowledge
Students are able to (all skills on K2 to K5):

  • Improve image quality problems that are related to adverse image capture conditions
  • Improve image quality using contrast enhancement, smoothing, and sharpening
  • Extract relevant entities (edges, corners, regions) from images
  • Represent regions using expressive features
  • Classify regions in images
  • Use suitable neural networks to master the tasks of image recognition, object detection, instance segmentation, and semantic segmentation
  • Compress images using suitable JPEG standards
  • Protect copyrights on images using digital watermarks
  • Image restoration in image and frequency domain
  • Image enhancement (contrast enhancement, image smoothing, image sharpening)
  • Edge and corner detection
  • Segmentation of images into regions
  • Feature extraction for object representation
  • Object classification
  • Image recognition using CNNs (Convolutional Neural Networks)
  • Object detection and segmentation using R-CNNs (Regions with CNN features)
  • Semantic segmentation using FCNs (Fully Convolutional Networks)
  • Image compression using JPEG standards
  • Copyright protection for images with digital watermarks
Criteria for evaluation Written exam at the end of the semester; realization, presentation and documentation of (small) practical project.
Methods Slide presentation with case studies on the blackboard; project
Language German
Study material PDF-versions of the slides used in the lecture will be made available via KUSSS.

Recommended reading:

  • Klaus D. Tönnies (2005). Grundlagender Bildverarbeitung. Person Studium.
Changing subject? No
Further information https://www.jku.at/institut-fuer-computational-perception/lehre/alle-lehrveranstaltungen/digitale-bildverarbeitung/
On-site course
Maximum number of participants -
Assignment procedure Direct assignment