COURSE UNIT TITLE

: DIGITAL IMAGE PROCESSING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5033 DIGITAL 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

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM) (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

The objectives of this course are to acquire an ability to identify an image enhancement/segmentation/representation/recognition problem and to make decision to offer a suitable solution to
it, an ability to design and conduct image processing (IP) solutions using a software package and to analyze and interpret processed image data, an ability to analyze the complexity and limitations of IP techniques, an understanding of the role of IP solutions in many fields.

Learning Outcomes of the Course Unit

1   To learn some image fundamentals, contrast enhancement techniques and their use, basic spatial domain filtering, line and edge detection
2   To learn 2-D Fourier transform, its properties, and frequency-domain filtering
3   To learn thresholding techniques and basic region-oriented segmentation techniques
4   To learn basic image segmentation, representation, description and recognition schemes
5   To learn basics of color image processing
6   To suggest a solution consisting of a set of proper IP techniques to a real world problem
7   To interpret and assess results of IP techniques and algorithms
8   To solve basic IP exercises using a software toolbox and write experimental work reports

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamentals of digital images
2 Sampling and quantization of images, connectivity, distance measures, arithmetic/logic operations
3 Contrast stretching, Histogram processing
4 Other image enhancement techniques, Spatial domain filtering
5 Discrete Fourier transform, 2-D Fourier transform and its properties, Fast Fourier transform
6 Frequency domain filtering, Line and Edge detection
7 Edge Linking, Thresholding
8 Optimal thresholding, Region-oriented image segmentation
9 Midterm exam
10 Motion segmentation, Color image fundamentals
11 Color image processing
12 Image representation schemes
13 Description schemes
14 Recognition methods

Recomended or Required Reading

Textbook: Digital Image Processing, R.C. Gonzalez, R.E. Woods, Prentice-Hall, 2002
References: Digital Image Processing Using Matlab, R.C. Gonzalez, R.E. Woods, S.L. Eddins, 2003
Materials: Lecture Notes

Planned Learning Activities and Teaching Methods

Lectures will be supported by homeworks prepared by students using Matlab applications

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 are evaluated by homeworks and exam questions.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

haldun.sarnel@deu.edu.tr

Office Hours

will be posted

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Tutorials 15 1 15
Preparations before/after weekly lectures 14 4 56
Preparation for midterm exam 1 18 18
Preparing assignments 1 24 24
Preparation for final exam 1 25 25
Midterm 1 4 4
Final 1 4 4
TOTAL WORKLOAD (hours) 188

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.13
LO.23
LO.33
LO.43
LO.53
LO.6332
LO.733
LO.833