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700.304 (17W) Fundamentals of Image Processing

Wintersemester 2017/18

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Erster Termin der LV
22.01.2018 09:00 - 18:00 B01.0.203 On Campus
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Überblick

Lehrende/r
LV-Titel englisch Fundamentals of Image Processing
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 11 (50 max.)
Organisationseinheit
Unterrichtssprache Englisch
mögliche Sprache/n der Leistungserbringung Deutsch
LV-Beginn 22.01.2018
eLearning zum Moodle-Kurs

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

The students

  • are able use the basic methods for image processing,
  • have a good command of the machine learning approaches for the description of image features and object recognition,
  • are able to transfer the acquired knowledge of image processing to solve complex applications for industry and research,
  • are able to describe the state-of-the-art of the presented topics

Lehrmethodik inkl. Einsatz von eLearning-Tools

The course has two major parts. The first part consists of the theoretical and methodic fundamentals that will be introduced during the lecture. The second part consists of intensive lab work where students can implement, test, and apply the presented methods. The major lab language will be Matlab but additional programming languages will be introduced like C++ (OpenCV) and Python.

Inhalt/e

  • Introduction (color systems & image formats                    
  • Image Transformations                                                    
  • Image Filtering                                                        
  • Morphology                                                      
  • Edge Detection                                                            
  • Polygon and Corner Detection                                                            
  • Image Interpolation                                                           
  • Invariant Features   
  • Camera Calibration                                                         
  • Scale invariant feature transform                                                       
  • Object Recognition
  • Autonomous Vehicles 

Erwartete Vorkenntnisse

Basic knowledge of any programming language

Literatur

Machine Vision, E.R. DaviesElsevier, Third EditionISBN-10: 0122060938ISBN-13: 978-0122060939Digital Image Processing (3rd Edition)[Hardcover] Rafael C. Gonzalez (Author), Richard E. Woods (Author)ISBN-10: 013168728XISBN-13: 978-0131687288

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

Written Exam + Project 

Prüfungsinhalt/e

The student should pass a written exam successfully which reflects the understanding of the presented concepts and approaches. Additionally, the students should design and implement a fully working system in the field of machine learning.

Beurteilungskriterien/-maßstäbe

The final evaluation is divided as follows:

70% Exam

30%  Project

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 17W.1)
    • Fach: Informationstechnik (Wahlfach)
      • 2.6 Bildverarbeitung ( 2.0h VC / 3.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
          Absolvierung im 5. Semester empfohlen
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 12W.1)
    • Fach: Informationstechnik (Wahlfach)
      • Bildverarbeitung ( 2.0h VO / 3.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
  • Bachelorstudium Informationstechnik (SKZ: 289, Version: 12W.2)
    • Fach: Informationstechnische Vertiefung (Wahlfach)
      • Wahl von Lehrveranstaltungen ( 0.0h VK/VO/KU / 6.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.304 Fundamentals of Image Processing (2.0h VC / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2022/23
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2021/22
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2020/21
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2019/20
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2018/19
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2016/17
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2015/16
  • 700.304 VC Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2014/15
  • 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2013/14
  • 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)
Wintersemester 2012/13
  • 700.304 VK Fundamentals of Image Processing (2.0h / 4.0ECTS)