• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Develop image processing algorithms to enhance digital images.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Formulate, perform and report experiments and produce prototypes
   4. Design and conduct research projects independently
   5. Find out new methods to improve his/her knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
2. Decide which method is appropriate to tackle a given image processing problem.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Manipulate knowledge and cooperate with multi-disciplines
   3. Acquire scientific knowledge
   4. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Laboratory exercise/exam
   3. Term paper
3. Develop image processing algorithms for automatic target detection applications

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Formulate, perform and report experiments and produce prototypes
   4. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   5. Acquire scientific knowledge
   6. Find out new methods to improve his/her knowledge
   7. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
4. Identify and exploit analogies between the mathematical tools used for 1D and 2D signal analysis and processing

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate, perform and report experiments and produce prototypes
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Manipulate knowledge and cooperate with multi-disciplines
   5. Design and conduct research projects independently
   6. Effectively express his/her research ideas and findings both orally and in writing
   7. Write progress reports clearly on the basis of published documents, thesis, etc
   8. Demonstrate professional and ethical responsibility

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Term paper
5. Analyze 2D signals in the frequency domain through the Fourier transform

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Formulate, perform and report experiments and produce prototypes
   4. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   5. Acquire scientific knowledge
   6. Develop an awareness of continuous learning in relation with modern technology
   7. Effectively express his/her research ideas and findings both orally and in writing
   8. Demonstrate professional and ethical responsibility

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Seminar/presentation
   4. Term paper
6. Implement digital image processing methods in MATLAB (or an equivalent programming language) based on a given algorithmic description or theory

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Manipulate knowledge and cooperate with multi-disciplines
   4. Acquire scientific knowledge
   5. Work effectively in multi-disciplinary research teams
   6. Develop an awareness of continuous learning in relation with modern technology
   7. Defend research outcomes at seminars and conferences

   Method of assessment

   1. Written exam
   2. Seminar/presentation
   3. Term paper

Contents
Week 1:	Introduction to digital image processing
Week 2:	Image enhancement using histogram processing
Week 3:	Image enhancement using spatial filtering
Week 4:	Image enhancement using fuzzy techniques
Week 5:	Filtering in the frequency domain
Week 6:	Implementation of filtering in the frequency domain
Week 7:	Midterm exam
Week 8:	Image segmentation (edge detection, thresholding)
Week 9:	Image segmentation (multiple thresholds, region-based thresholding)
Week 10:	Morphological image processing (binary, grayscale)
Week 11:	Image restoration (noise models, restoration by spatial filtering)
Week 12:	Image restoration (restoration by frequency domain filtering, Wiener Filtering)
Week 13:	Color image processing
Week 14:	Presentation of term project to class
Week 15*:	Review
Week 16*:	Final Exam
Textbooks and materials:	Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Third Edition, Prentice Hall, 2008.
Recommended readings:	Gonzalez, Woods, and Eddins, “Digital Image Processing using MATLAB”, Second Edition, Gatesmark Publishing, 2009.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	6	15
Other in-term studies:	2,4,7,10,11	10
Project:	3,9	40
Homework:	4,8,9,12	10
Quiz:		0
Final exam:	16	25
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	12
Practice, Recitation:	0	0
Homework:	4	9
Term project:	18	2
Term project presentation:	0.5	2
Quiz:	0	0
Own study for mid-term exam:	12	1
Mid-term:	1	1
Personal studies for final exam:	15	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)