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700.470 (20S) Artificial Vision

Sommersemester 2020

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Erster Termin der LV
13.05.2020 09:00 - 12:00 online Off Campus
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch Artificial Vision
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 10 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
mögliche Sprache/n der Leistungserbringung Englisch
LV-Beginn 13.05.2020
eLearning zum Moodle-Kurs
Seniorstudium Liberale Ja

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Intendierte Lernergebnisse

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

Gaining experience in programming vision algorithms

Lehrmethodik inkl. Einsatz von eLearning-Tools

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)


Erwartete Vorkenntnisse

UPDATED SCHEDULE, May 06, 2020

Wed 13/5: 
09:00 - 12:00 and 14:00  18:00 

Thu 14/5:  
09:00 -13:00  and 14:00 - 18:00 

Fri 15/5:
09:00 - 13:00 and 14:00 - 18:00 

UPDATE April2020:

Please be informed that the guest lecture on "Artificial Vision" will take place as planned and on dates/time as announced although it will be offered online! We will inform you with further details in due time!



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.

Geänderte Prüfungsinformationen (COVID-19 Ausnahmeregelung)

Mode of Exam: 

a.)  presentation in the last session of the course, 

b.) programming project (which will expand the topic discussed in the presentation at the end of last lesson)

c.) written project report to be uploaded to Moodle 

 

Deadline for both top b.) and top c.) -> June 25, 2020; 23.59hrs

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JUNE 16, 2020: DEADLINE for both top b.) and c.) EXTENDED TO JULY 31, 2020; 23:59hrs

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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 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2022
  • 700.470 VC Artificial Vision (2.0h / 4.0ECTS)
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)