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

Sommersemester 2014

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Erster Termin der LV
04.03.2014 13:00 - 15:00 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

5 (20 max.)

Organisationseinheit

Institut für Intelligente Systemtechnologien

Unterrichtssprache

Englisch

LV-Beginn

01.03.2014

Zeit und Ort

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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

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.

Deutsch
Englisch

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 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)