700.373 (13S) Labor: Machine Vision and Smart Sensors for Intelligent Vehicles

Sommersemester 2013

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Erster Termin der LV
05.03.2013 12:00 - 14:00 L4.1.02 (ICT Lab) Off Campus
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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 14 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 01.03.2013

Zeit und Ort

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LV-Beschreibung

Inhalt/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. Also we deliver some pattern recognition and machine learning methods to build a smart vision system. Student can practice how to implement image processing algorithms in Matlab and OpenCV with C#

Themen

  • Introduction to Machine Vision
  • Object Representation and Description
  • Spatial Processing
  • Frequency Domain Processing
  • Image Restoration
  • Advanced Filtering Techniques (Diffusion filtering).
  • Segmentation
  • Advanced Segmentation Methods (Mean shift, Active contours, Watersheds)
  • Camera vision (2D&3D)
  • Image Stitching (Panorama)
  • Motion Estimation and Object tracking(Kalman Tracking, Optical Flow)
  • Visual Pattern Recognition (PCA, LDA, SVM)
  • Statistical Shape Modeling (AAM,ASM)
  • Some Medical Imaging Examples

Lehrziel

Build smart vision systems using Matlab or C#

Erwartete Vorkenntnisse

Basics in Mathematic, Familiar with Matlab programming or .NET

Literatur

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: 978-1907920097

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.

Beurteilungskriterien/-maßstäbe

Project + Presentation

Beurteilungsschema

Note Benotungsschema

Position 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)
  • 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)
  • 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)

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)
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 2014
  • 700.373 KU Labor: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h / 3.0ECTS)