700.470 (24S) Artificial Vision

Sommersemester 2024

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Erster Termin der LV
14.05.2024 09:00 - 12:00 B04.1.03 On Campus
Nächster Termin:
14.05.2024 13:00 - 16:00 B04.1.03 On Campus

Überblick

Lehrende/r
LV-Titel englisch Artificial Vision
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
LV-Modell Präsenzlehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 14 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
mögliche Sprache/n der Leistungserbringung Englisch
LV-Beginn 14.05.2024
eLearning zum Moodle-Kurs

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Intendierte Lernergebnisse

Understanding the fundamentals and key processing steps of artificial vision and deep learning-based systems. Gaining experience in programming deep learning algorithms for computer vision tasks.


IMPORTANT:

All units might include a little of programming. Please bring your own laptop with Python and PyTorch installed. 

Python: https://www.python.org/downloads/

PyTorch: https://pytorch.org/



Lehrmethodik

Lecture
 Student presentations
 Programming units

Inhalt/e

Abstract: 

The lecture series will introduce the pipeline and the modules to develop advanced artificial vision-based systems. Lectures will cover computer vision concepts, from the early stages of image formation, filtering, and feature extractions to the most advanced deep learning techniques for image and video interpretation and understanding. The course will discuss the state-of-the-art algorithms for recognizing, detecting, segmenting, and tracking objects and understanding their activities. A sufficient number of hours will be reserved for laboratory activities allowing the implementation of cutting-edge algorithms.

Outline: 

  • Introduction of computer vision and AI (for computer vision)
  • Computational pipeline of an artificial vision systems
  • Fundamentals of computer vision
    • Image formation
    • Filtering
    • Image features
    • Neural networks and deep learning
  • Computer vision in the modern era (with a Python laboratory perspective)
    • Object recognition
    • Object detection
    • Object segmentation
    • Object Tracking

Keywords: Computer Vision, Image Processing, Object Recognition, Object Detection, Object Segmentation, Object Tracking, Video Surveillance, Machine Learning, Deep Learning, Multimedia processing

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

Student presentation & and Programming project 

A presentation in the last session of the course that describes the problem to be addressed and possible ways to solve it;

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

written project report to be uploaded to Moodle (report due by June 28,  2024) 


Prüfungsinhalt/e

topics of the lecture

Beurteilungskriterien/-maßstäbe

Analysis and solution of a selected topic:

quality of the presentation (25%) 

quality and scope of the programming project (50%) 

quality of report (25%) 

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Artificial Intelligence and Cybersecurity (SKZ: 993, Version: 20W.1)
    • Fach: Specialisation in Artificial Intelligence and Cybersecurity (Wahlfach)
      • Fachlich relevante Lehrveranstaltungen ( 0.0h XX / 34.0 ECTS)
        • 700.470 Artificial Vision (2.0h VC / 4.0 ECTS)
          Absolvierung im 2., 3. Semester empfohlen
  • 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: 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: 22W.1)
    • Fach: Information and Communicatons Enginnering: Supplements (Wahlfach)
      • 1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) ( 0.0h VC, KS / 14.0 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 2020
  • 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)