700.470 (20S) Artificial Vision

Sommersemester 2020

Ende der Anmeldefrist
31.03.2020 23:59

Erster Termin der LV
13.05.2020 09:00 - 18:00 , B04a.1.02
Nächster Termin:
14.05.2020 09:00 - 18:00 , B04a.1.02

Überblick

Lehrende/r
LV-Titel englisch
Artificial Vision
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
4 (20 max.)
Organisationseinheit
Unterrichtssprache
Englisch
mögliche Sprache/n der Leistungserbringung
Englisch
LV-Beginn
13.05.2020
eLearning
zum Moodle-Kurs
Seniorstudium Liberale
Ja

LV-Beschreibung

Intendierte Lernergebnisse

Understanding the fundamentals and key processing steps of artificial vision-based systems

Gaining experience in programming vision algorithms

Lehrmethodik

Lecture

Student presentations

Programming units

Inhalt/e

Artificial Vision Guest Lecture Series 2020

The lecture series will introduce the pipeline and the modules to develop advanced artificial vision based systems. From the early stages of image creation to the most advanced techniques for image and video interpretation the course will propose the state of the art algorithms for detecting objects and understanding their activities. A sufficient amount of hours will be reserved to laboratory activities allowing to implement some algorithms.

Outline:

  • Introduction
  • Computational pipeline of an artificial vision systems
  • Low level processing (object segmentation, object detection, etc.)
    Image differencing (Frame to Background; Frame by frame); Background Updating; Thresholding (Image registration; Translation; Affine; Homography)
    Feature based image registration (Feature tracking; Outlier detection; Transform Computation)
    Stereo Vision
  • Middle level processing (object recognition, object tracking, etc.)
    Space projection (Principal Component Analysis, Linear Discriminant Analysis, etc.)
    Machine Learning for object recognition (neural networks, neural trees, etc.)
    Deep learning for computer vision
  • High level processing (behaviour analysis, event detection, etc.)
    Architecture for behaviour analysis
    Simple events
    Complex events
    Feature extraction
    Trajectory analysis
    Clustering
    Decision making
  • Network reconfiguration
    Modeling of a sensor network
    Resources Optimization
  • Python  Laboratory
    Change Detection
    Image Mapping
    Image Registration (Mosaicking)


Prüfungsinformationen

Prüfungsmethode/n

Student presentation

and

Programming project or written exam

Prüfungsinhalt/e

topics of the lecture

Beurteilungskriterien/-maßstäbe

quality of the presentation

quality and scope of the programming project or written exam


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) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.470 Artificial Vision (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)
        • 700.470 Artificial Vision (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)
        • 700.470 Artificial Vision (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)
        • 700.470 Artificial Vision (2.0h VC / 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)
        • 700.470 Artificial Vision (2.0h VC / 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)
        • 700.470 Artificial Vision (2.0h VC / 4.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2018
  • 700.470 VC Artificial Vision (2.0h / 4.0ECTS)
Sommersemester 2016
  • 700.470 VC Artificial Vision (2.0h / 4.0ECTS)
Sommersemester 2014
  • 700.470 VK Artificial Vision (2.0h / 4.0ECTS)
Sommersemester 2013
  • 700.470 VK Artificial Vision (2.0h / 4.0ECTS)