COURSE UNIT TITLE

: IMAGE PROCESSING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5016 IMAGE PROCESSING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR SEDAT ÇAPAR

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The aim of the course is to teach basic image processing methods and algorithms.

Learning Outcomes of the Course Unit

1   To learn the basic concepts of image processing.
2   To learn the basic issues of Image processing like hardware, software, digitization,
3   Learn how to apply to real samples that require image processing.
4   To be able to analyze and program image processing algorithms.
5   To be able to prepare project related to image processing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction
2 Image Fundamentals
3 Image Enhancement Techniques
4 Filtering
5 Image Enhancement Techniques
6 Filtering in Frequency Domain
7 Color Image Processing
8 Midterm
9 Image Segmentation 1
10 Image Segmentation 2
11 Morphological Image Processing
12 Image Representation

Recomended or Required Reading

Digital Image Processing 3rd Edition, Rafael C. Gonzalez, Richard E. Woods, Addison Wesley, 2008

Planned Learning Activities and Teaching Methods

The course consists of lecture and projects.

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 * 0.30 +ASG * 0.20 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homework/presentation

Language of Instruction

Turkish

Course Policies and Rules

Course Policies and Rules:
Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the graduate policy at http://www.fbe.deu.edu.tr/

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics
e-mail: sedat.capar@deu.edu.tr
phone: +90 232 301 8601

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
0
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 40 40
Preparation for final exam 1 45 45
Preparing assignments 1 25 25
Other activities within the scope of the atelier pratices 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1455
LO.2455
LO.345555
LO.445544
LO.54555544

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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