700.373 (14S) Labor: Machine Vision and Smart Sensors for Intelligent Vehicles
Überblick
- Lehrende/r
- LV-Titel englisch Lab: Machine Vision and Smart Sensors for Intelligent Vehicles
- LV-Art Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 5 (20 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 01.03.2014
Zeit und Ort
Liste der Termine wird geladen...
LV-Beschreibung
Inhalt/e
This Lab delivers an overview of machine vision and image processing in in C#. Our focus will be in some basic and advanced methods such us Image enhancement, Houch transform , Edge detection, face detection. Handwriting recognition, Active apprience modeling, 3D reconstruction. and image enhancement.Themen
- Introduction to Machine Vision in C#
- Spatial & Frequency domain in C#
- Image filtering in C#
- Edge detection and low level segmentation in C#
- Object description and representation in C#
- Advanced Segmentation Methods (Mean shift, Active contours, Watersheds) in C#
- Camera vision (2D&3D) in C#
- Motion estimation and object tracking(Kalman Tracking, Optical Flow) in C#
- Visual Pattern Recognition (PCA, LDA, SVM,ANN) in C#
- Statistical Shape Modeling (AAM,ASM) in C#
Lehrziel
Build smart vision systems using C#/C++Erwartete Vorkenntnisse
Basics in Mathematic, familar with .NetLiteratur
Book-1: Digital Image Processing (2nd Edition) • Publisher: Prentice Hall; 2nd edition (January 15, 2002) Language: English ISBN-10: 0201180758 ISBN-13: 978-0201180756 Book-2: Image Processing: The Fundamentals • Publisher: Wiley; 2 edition (May 17, 2010) • Language: English • ISBN-10: 047074586X • ISBN-13: 978-0470745861 Book-3: Design your own PC Visual Processing and Recognition System in C# • ISBN-10: 1907920099 • ISBN-13: 978-1907920097Inhalt/e
This Lab delivers an overview of machine vision and image processing. Our focus will be in some basic and advanced methods such us Image enhancement, Houch transform , Edge detection, face detection. Handwriting recognition, Active apprience modeling, 3D reconstruction. and image enhancement.Themen
- Introduction to Machine Vision
- Spatial & Frequency domain
- Image filtering
- Edge detection and low level segmentation
- Object description and representation
- Image restoration
- Advanced filtering techniques (Diffusion filtering).
- Advanced Segmentation Methods (Mean shift, Active contours, Watersheds)
- Camera vision (2D&3D)
- Motion estimation and object tracking(Kalman Tracking, Optical Flow)
- Visual Pattern Recognition (PCA, LDA, SVM,ANN)
- Statistical Shape Modeling (AAM,ASM)
Lehrziel
Build smart vision systems using C#/C++Erwartete Vorkenntnisse
Basics in Mathematic, Familiar with .NET programmingLiteratur
Book-1: Digital Image Processing (2nd Edition) • Publisher: Prentice Hall; 2nd edition (January 15, 2002) Language: English ISBN-10: 0201180758 ISBN-13: 978-0201180756 Book-2: Image Processing: The Fundamentals • Publisher: Wiley; 2 edition (May 17, 2010) • Language: English • ISBN-10: 047074586X • ISBN-13: 978-0470745861 Book-3: Design your own PC Visual Processing and Recognition System in C# • ISBN-10: 1907920099 • ISBN-13: 978-1907920097Prüfungsinformationen
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.
Beurteilungskriterien/-maßstäbe
Project + Presentation
Beurteilungskriterien/-maßstäbe
Project + PresentationBeurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Technischer Schwerpunkt (Intelligent Transportation Systems)
(Pflichtfach)
-
1.4-1.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
- 700.373 Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)
-
1.4-1.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
-
Fach: Technischer Schwerpunkt (Intelligent Transportation Systems)
(Pflichtfach)
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Technischer Schwerpunkt (Media Engineering)
(Pflichtfach)
-
1.4-1.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
- 700.373 Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)
-
1.4-1.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
-
Fach: Technischer Schwerpunkt (Media Engineering)
(Pflichtfach)
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Technische Ergänzung II
(Pflichtfach)
-
3.4-3.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
- 700.373 Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)
-
3.4-3.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)
-
Fach: Technische Ergänzung II
(Pflichtfach)
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Freie Wahlfächer
(Freifach)
-
Diverse Lehrveranstaltungen (
0.0h VO/VK/VS/KU/PS / 12.0 ECTS)
- 700.373 Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)
-
Diverse Lehrveranstaltungen (
0.0h VO/VK/VS/KU/PS / 12.0 ECTS)
-
Fach: Freie Wahlfächer
(Freifach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 700.373 KS Lab: Neurocomputing in Robotics and Intelligent Transportation (2.0h / 3.0ECTS)
-
Sommersemester 2023
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
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Sommersemester 2022
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2021
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2020
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2019
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2018
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2017
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2016
- 700.373 KS Lab on Machine Learning and Applications in Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2015
- 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)
-
Sommersemester 2013
- 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)