Communication and Media Engineering

Application
EMI Department

Module Guide

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Digital Image Processing

Prerequisite

Linear Algebra
Teaching methods Lecture/Lab
Learning target / Competences

Target skills:

The student will gain an overview on established and modern image processing techniques. The course provides tools, methods, models and techniques for the following topics: image formation, optics, imagers, color, image segmentation, image analysis, image features, image alignment, estimation in computer vision, programming and deep learning.

 

Competences:

The student will understand basic problems in image processing and machine vision, e.g. image segmentation, feature detection, image matching or estimation problems in alignment.

He/she will know methods, algorithms and common techniques to solve the above mentioned problems.

The student will be able to computationally apply the methods on given low-level and higher-level image processing tasks in real world computer vision problems.

 
Duration 1
Hours per week 4.0
Overview
Classes 60 h
Individual / Group work: 60 h
Workload 120 h
ECTS 4.0
Requirements for awarding credit points

Digital Image Processing: written exam K60
DIP Lab must be passed.
Credits and grades

4 CP, grades 1 ... 5
Responsible person

Prof. Dr.-Ing. Stefan Hensel
Recommended semester 3
Frequency Every 2nd sem.
Usability

Master's degree program CME, EIM and MMR
Lectures

DIP Lab

Type Lab
Nr. EMI417
Hours per week 1.0
Content

Programming of Image Processing Algorithms with Matlab
Projects from the fields of:

  • Image types
  • Color channels
  • Linear filters
  • Morphological Operators (Hit or Miss)
  • Hough-Transformation
  • Feature extraction
  • Image alignment

 
Literature

Laboratory hand-outs
Szeliski, R., Computer Vision: Algorithms and Applications, Springer, 2010
Jähne, B., Digital Image Processing, Springer 2012
Erhardt, A., Einführung in die Digitale Bildverarbeitung, Vieweg+Teubner, 2008
Gonzalez, Digital Image Processing using Matlab, Addison Wesley, 2004

 

Digital Image Proc.

Type Lecture
Nr. EMI416
Hours per week 3.0
Content

The lecture covers the following topics

  1. Image Formation
    • The optical system, pinhole model
    • Photosensitive sensors, CCD and CMOS
    • Digitalization and quantization
    • Aliasing-Effects
    • Colors, Bayer-Filter
  2. Image Preprocessing
    • Image histogram
    • 2D-Fouriertransformation
    • Linear filters, point operators, rank order filters
  3. Image Features
    • Edges
    • Corners
  4. Image Mosaicing 
    • Detectors and Descriptors
      • Canny edge detector
      • Harris corner detector
      • Blob detectors, Laplacian of Gaussians
      • SIFT detector and descriptor
    • Image transformations
    • Image alignment
      • Least squares estimation
      • Robust estimation, RANSAC

 
Literature

Szeliski, R., Computer Vision: Algorithms and Applications, Springer, 2010
Jähne, B., Digital Image Processing and Image Formation, Springer 2012
Forsyth, D., COmputer Vision: A Modern Approach, Addison Wesley, 2012
Hartley, R., Zisserman, A., Multiple View Geometry in Computer Vision, 2nd ed.,Cambridge University Press, 2004

 
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