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

Sommersemester 2015

Anmeldefrist abgelaufen.

Erster Termin der LV
03.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab Off Campus
... 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-Anrechnungspunkte 3.0
Anmeldungen 7 (20 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 03.03.2015

Zeit und Ort

Liste der Termine wird geladen...

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

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

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 + 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: 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 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 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)