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

Sommersemester 2015

Registration deadline has expired.

First course session
03.03.2015 12:00 - 14:00 L4.1.02 ICT-Lab Off Campus

... no further dates known

Overview

Lecturer

Course title german

Labor: Machine Vision and Smart Sensors for Intelligent Vehicles

Type

Course (continuous assessment course )

Hours per Week

2.0

ECTS credits

3.0

Registrations

7 (20 max.)

Organisational unit

Institut für Intelligente Systemtechnologien

Language of instruction

English

Course begins on

03.03.2015

Time and place

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Course content

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.

Topics

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.. )

Teaching objective

Build smart vision systems using Matlab/Java/CUDA

Literature

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

Course content

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.

Topics

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)

Teaching objective

Build smart vision systems using C#/C++

Prior knowledge expected

Basics in Mathematic, Familiar with .NET programming

Literature

Examination information

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.

German
English

Assessment criteria / Standards of assessment for examinations

Project + Presentation

Assessment criteria / Standards of assessment for examinations

Project + Presentation

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
- Subject: Major Field of Specialization (Intelligent Transportation Systems) (Compulsory subject)
  - 1.4-1.5 Exercises or Lab ( 4.0h KU / 6.0 ECTS)
    - 700.373 Lab: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)

Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
- Subject: Major Field of Specialization (Media Engineering) (Compulsory subject)
  - 1.4-1.5 Exercises or Lab ( 4.0h KU / 6.0 ECTS)
    - 700.373 Lab: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)

Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
- Subject: Additional Technical Module II (Compulsory subject)
  - 3.4-3.5 Exercises or Lab ( 4.0h KU / 6.0 ECTS)
    - 700.373 Lab: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)

Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
- Subject: Electives (Optional subject)
  - Diverse Lehrveranstaltungen ( 0.0h VO/VK/VS/KU/PS / 12.0 ECTS)
    - 700.373 Lab: Machine Vision and Smart Sensors for Intelligent Vehicles (2.0h KU / 3.0 ECTS)

Equivalent courses for counting the examination attempts

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)