700.373 (15S) Labor: Machine Vision and Smart Sensors for Intelligent Vehicles

Sommersemester 2015

Anmeldefrist abgelaufen.
... 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
7 (20 max.) Anzahl der tatsächlich angemeldeten Studierenden
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
01.03.2015
eLearning
zum Moodle-Kurs

Zeit und Ort

Tag von - bis Raum Details
Di, 03.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 10.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 17.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 24.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 14.04.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 21.04.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 28.04.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 05.05.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 12.05.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 19.05.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 02.06.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 09.06.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 16.06.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 23.06.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich
Di, 30.06.2015 12:00 - 14:00 L4.1.02 ICT-Lab wöchentlich

LV-Beschreibung

Inhalt/e

This Lab delivers an overview of machine vision and image processing in Matlab and JAVA. Our focus will be in some advanced methods such us Image enhancement, Handwriting recognition, Active apprience modeling, 3D reconstruction. and image enhancement.

Themen

  • Advanced Segmentation Methods (Mean shift, Active contours, Watersheds) in C#
  • Camera vision (2D&3D) in
  • Visual Pattern Recognition (PCA, LDA, SVM,ANN)
  • Statistical Shape Modeling (AAM,ASM, LCM)
  • Convolutional Neural Network
  • Image Processing with CUDA
  • Image Processing with Torch7 (Lua based library. An Artificial Intelligence library used by Google, Facebook etc.. )

Lehrziel

Build smart vision systems using Matlab/Java/CUDA

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

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 keine Anmeldevoraussetzung

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: 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)
  • 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

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 2014
  • 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)