Communication and Media Engineering

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Digital Image Processing

Empfohlene Vorkenntnisse

Linear Algebra
Lehrform Vorlesung/Labor
Lernziele / Kompetenzen

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.

 
Dauer 1
SWS 4.0
Aufwand
Lehrveranstaltung 60 h
Selbststudium / Gruppenarbeit: 60 h
Workload 120 h
ECTS 4.0
Voraussetzungen für die Vergabe von LP

Digital Image Processing: written exam K60
DIP Lab must be passed.
Leistungspunkte Noten

4 CP, grades 1 ... 5
Modulverantwortlicher

Prof. Dr.-Ing. Stefan Hensel
Empf. Semester 3
Haeufigkeit jedes 2. Semester
Verwendbarkeit

Master-Studiengang CME
Veranstaltungen

DIP Lab

Art Labor
Nr. EMI417
SWS 1.0
Lerninhalt

•  Programming of Image Processing Algorithms with Matlab

•  Projects from the fields of:

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

 
Literatur

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.

Art Vorlesung
Nr. EMI416
SWS 3.0
Lerninhalt

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 

 
Literatur

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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