COURSE UNIT TITLE

: MACHINE VISION IN MECHATRONICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MEC 5006 MACHINE VISION IN MECHATRONICS ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASISTANT PROFESSOR HATICE DOĞAN

Offered to

M.Sc. Mechatronics Engineering
Mechatronics Engineering

Course Objective

The aim of this course is to introduce the theory, applications and techniques of machine vision to the students

Learning Outcomes of the Course Unit

1   Be able to understand basic definitions and concepts of machine vision systems.
2   Be able to define and select components of a machine vision system.
3   Be able to apply basic image processing techniques for machine vision applications.
4   Be able to understand the possibilities and limitations of application of image processing and machine vision.
5   Be able to implement the machine vision algorithms for real world applications

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Machine Vision
2 The Image, its Representations and Properties I
3 The Image, its Representations and Properties II (Assignment)
4 Image Pre- Processing
5 Image Enhancement (Assignment)
6 Mathematical Morphology-I
7 Mathematical Morphology-II (Assignment)
8 Midterm
9 Segmentation I
10 Segmentation II (Assignment)
11 Object recognition
12 Shape Representation and Description (Assignment)
13 Motion, optical flow, vision based control algorithms
14 Region and people tracking (Assignment)

Recomended or Required Reading

M. Sonka , V. Hlavac, R. Boyle, Image processing, Analysis, and Machine Vision, 3ed, Thomson, 2008.

Recommended readings:

R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd Ed., Prentice Hall, 2007.
J. Billingsley, R. Bradbeer, Mechatronics and Machine Vision in Practice, Springer-Verlag 2008.
R. Szeliski, Computer Vision: Algorithms and Applications, Springer. 2010.
D. H.Ballard, C. M.Brown, Computer Vision, Prentice-Hall, 2003.
L. Shapiro, G. Stockman, Computer Vision, Prentice-Hall, 2001.

Planned Learning Activities and Teaching Methods

A series of lectures on course materials will be given using PowerPoint presentations and blackboard.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcomes will be evaluated by examinations and assignments.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Hatice Doğan
hatice.dogan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparing assignments 6 8 48
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 12 12
Preparation for final exam 1 18 18
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 175

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.14
LO.233
LO.33323
LO.4432
LO.53323

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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