COURSE UNIT TITLE

: DIGITAL IMAGE PROCESSING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EED 4014 DIGITAL IMAGE PROCESSING ELECTIVE 3 2 0 6

Offered By

Electrical and Electronics Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

Offered to

Electrical and Electronics Engineering

Course Objective

The objectives of this course are an to acquire an ability to identify an image enhancement/segmentation problem and to make decision to offer a suitable solution to it, an ability to design and conduct image processing (IP) experiments 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, an ability to write work and selfassessment reports.

Learning Outcomes of the Course Unit

1   To learn some image fundamentals: the effect of sampling and quantization on image quality, connectivity and connected component labeling
2   To understand contrast enhancement techniques and their use
3   To learn 2-D Fourier transform, its properties, and frequency-domain filtering
4   To learn basic spatial-domain filtering, line and edge detection techniques
5   To learn thresholding techniques and basic region-oriented segmentation techniques
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 in a guided experimental way and write experimental work reports and self-assessment 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
3 Connectivity, distance measures, arithmetic/logic operations
4 Contrast stretching
5 Histogram processing
6 Local enhancement, other image enhancement techniques
7 Spatial domain filtering
8 Midterm exam
9 2-D Fourier transform and its properties
10 Frequency domain filtering
11 Line and Edge detection
12 Edge Linking
13 Thresholding
14 Region-oriented image segmentation

Recomended or Required Reading

Textbook: Digital Image Processing, R.C. Gonzalez, R.E. Woods, Prentice-Hall, 2002
Yardımcı kaynaklar: Digital Image Processing Using Matlab, R.C. Gonzalez, R.E. Woods, S.L. Eddins, 2003
Referanslar:
Diğer ders materyalleri:Lecture Notes

Planned Learning Activities and Teaching Methods

Lectures will be supported by homeworks, recitation hours, and Matlab applications in computer lab.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 LAB LABORATORY
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE * 0.25 + ASG * 0.10 + LAB * 0.15 + FIN * 0.50
6 RST RESIT
7 FCG FINAL COURSE GRADE MTE * 0.25 + ASG * 0.10 + LAB * 0.15 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

Students will be given homeworks to help them acquire learning outcomes. The homeworks prepared by them will be graded. In lab hours, experiments will be performed by students to help understand image processing algorithms and interpret their results. The lab reports prepared by them will be graded.

Assessment Criteria

Learning outcomes are evaluated by homeworks, laboratory appilicatins, 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 14 2 28
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 12 12
Preparation for final exam 1 18 18
Preparing assignments 2 5 10
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 156

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.133
LO.233
LO.333
LO.423
LO.523
LO.634413
LO.7331
LO.833314343