700.373 (14S) Labor: Machine Vision and Smart Sensors for Intelligent Vehicles

Sommersemester 2014

Anmeldefrist abgelaufen.

Erster Termin der LV
04.03.2014 13:00 - 15:00 , L4.1.02 ICT-Lab
... keine weiteren Termine bekannt

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-Anrechungspunkte
3.0
Anmeldungen
5 (20 max.)
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
01.03.2014
eLearning
zum Moodle-Kurs

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 .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

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.

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 programming

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

Beurteilungskriterien/-maßstäbe

Project + Presentation

Beurteilungskriterien/-maßstäbe

Project + Presentation

Beurteilungsschema

Note/Grade 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: 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)
  • 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 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)