700.340 (16S) Machine Vision in Intelligent Transportation

Sommersemester 2016

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Überblick

Lehrende/r
LV-Titel englisch
Machine Vision in Intelligent Transportation
LV-Art
Vorlesung-Seminar (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
9 (20 max.) Anzahl der tatsächlich angemeldeten Studierenden
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
08.03.2016
eLearning
zum Moodle-Kurs
Seniorstudium Liberale
Ja

Zeit und Ort

Tag von - bis Raum Details
Mo, 14.03.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 04.04.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 11.04.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 18.04.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 25.04.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 02.05.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 09.05.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 23.05.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 30.05.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 06.06.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 13.06.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 20.06.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Mo, 27.06.2016 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich

LV-Beschreibung

Inhalt/e

The seminar has mainly two different parts. Part one covers different advanced topics in machine vision based on different lectures that will be held during the semster. In part two, students will be prepared to be able to choose a paper in the field of machine vision and try to implement it using MATLAB, OPENCV or .net EMGU. The overall structure for part two is: # A list of topics will be suggested and placed in Moodle. # To each topic 1 or 2 basic papers will be suggested, which contains basic related information (will be placed in Moodle) # A student selects a topic (groups of 2 students are allowed) # For each topic, 12 to 15 slides will be prepared by the students. # Dates for Mini seminar presentations: in the last 2 weeks of the semester will be announced

Themen

  • Object Segmentation
  • Object Recognition
  • Object Tracking
  • Face Recognition
  • Image Stitching
  • Video Understanding
  • Event Detection
  • Sensor Fusion

Lehrziel

The class handels advanced topics in Image processing and video understanding.

Inhalt/e

The seminar has mainly two different parts. Part one covers different advanced topics in machine vision based on different lectures that will be held during the semster. In part two, students will be prepared to be able to choose a paper in the field of machine vision and try to implement it using MATLAB, OPENCV or .net EMGU. The overall structure for part two is: # A list of topics will be suggested and placed in Moodle. # To each topic 1 or 2 basic papers will be suggested, which contains basic related information (will be placed in Moodle) # A student selects a topic (groups of 2 students are allowed) # For each topic, 12 to 15 slides will be prepared by the students. # Dates for Mini seminar presentations: in the last 2 weeks of the semester will be announced

Themen

  • Object Segmentation
  • Object Recognition
  • Object Tracking
  • Face Recognition
  • Image Stitching
  • Video Understanding
  • Event Detection
  • Sensor Fusion

Lehrziel

The class handels advanced topics in Image processing and video understanding.

Prüfungsinformationen

Beurteilungskriterien/-maßstäbe

Presentation + Report

Beurteilungskriterien/-maßstäbe

Presentation + Report

Beurteilungsschema

Note/Grade Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Information and Communications Engineering: Supplements (NC, ASR)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics: Advanced (ASR)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics (WI)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Free Electives
      • Free Electives ( 0.0h XX / 6.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung I (Pflichtfach)
      • 2.3 Vorlesung mit Kurs oder Seminar ( 2.0h VK/SE / 4.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung I (Pflichtfach)
      • 2.1-2.2 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 4.0h VK/VS / 8.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung II (Pflichtfach)
      • 3.1-3.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Research Track (Methodischer Schwerpunkt) (Pflichtfach)
      • 4.2'-4.3' Theoretisch-Methodische Lehrveranstaltung I/II ( 0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Freie Wahlfächer (Freifach)
      • Diverse Lehrveranstaltungen ( 0.0h VO/VK/VS/KU/PS / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Media Engineering) (Pflichtfach)
      • 1.1-1.3 Vorlesung mit Kurs oder Vorlesung mit Seminar ( 6.0h VK/VS / 12.0 ECTS)
        • 700.340 Machine Vision in Intelligent Transportation (2.0h VS / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2017
  • 700.340 VS Machine Learning in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2015
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2014
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)
Sommersemester 2013
  • 700.340 VS Machine Vision in Intelligent Transportation (2.0h / 4.0ECTS)