700.470 (24S) Artificial Vision
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
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
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 BenotungsschemaPosition 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
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- 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)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
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)