700.470 (22S) Artificial Vision
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Artificial Vision
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 21 (20 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Englisch
- LV-Beginn 09.05.2022
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
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
Abstract:
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)
Keywords: Computer Vision, Image Processing, Object Detection & Recognition, Video Surveillance, Machine Learning, Video Networks
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)
Deadline June 20, 2022; 23:59pm
or written exam
July 07, 2022; 09:00 - 11:00am
Prüfungsinhalt/e
topics of the lecture
Beurteilungskriterien/-maßstäbe
quality of the presentation
quality and scope of the programming project or written exam
Beurteilungsschema
Note BenotungsschemaPosition 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)
-
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: 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (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: 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 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)