GRADUATE SCHOOL

M.SC. in Bioengineering (With Thesis)

EEE 501 | Course Introduction and Application Information

Course Name
Applied Digital Image Processing
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
EEE 501
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course aims to teach the fundamental principles and techniques of digital image processing and their applications to engineering problems.The course covers topics such as point operations, image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching, and software applications
Learning Outcomes The students who succeeded in this course;
  • know how to use the principles of image formation, sampling and quantization while analyzing image processing techniques and systems,
  • know how to use intensity transformations, spatial- and frequency-domain filtering techniques to process and enhance images,
  • know how to use specific techniques to restore images in the presence of different noise and degradation processes,
  • know how to apply the most common color models and use them in color image processing,
  • have knowledge of the advantages and limitations of different digital image processing algorithms,
  • be able to evaluate potential applications of digital image processing techniques,
  • be able to design and implement digital image processing algorithms using Matlab and image processing toolbox.
Course Description Image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction, Application Areas of Digital Image Processing Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 1)
2 Digital Image Fundamentals. Sampling, Quantization, Aliasing, Moire patterns Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 2)
3 Point Operations,Image Intensity Transformations, Histogram Processing Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 3)
4 Spatial Filtering, Image Enhancement in Spatial Domain Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 3)
5 Frequency Domain Filtering and Image Enhancement Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 4)
6 Image Restoration and Reconstruction Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 5)
7 Image Restoration and Reconstruction Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 5)
8 Midterm
9 Color Image Processing, Color Transformations
10 Multiresolution Image Processing Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 6)
11 Image Compression Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch.7)
12 Morphological Image Processing Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 9)
13 Image Segmentation and Registration Gonzales & Woods, Digital Image Processing, Prentice Hall,3rd ed., 2008 (Ch. 10)
14 In-class Presentations
15 In-class Presentations
16 Review of the Semester  

 

Course Notes/Textbooks The textbook referenced above and course slides
Suggested Readings/Materials Related Research Papers

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
-
-
Laboratory / Application
-
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
30
Presentation / Jury
Project
1
40
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
Total

Weighting of Semester Activities on the Final Grade
3
100
Weighting of End-of-Semester Activities on the Final Grade
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
16
3
48
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
20
20
Presentation / Jury
0
Project
1
89
89
Seminar / Workshop
0
Oral Exam
0
Midterms
1
20
20
Final Exam
0
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to have adequate knowledge in Mathematics, Life Sciences and Bioengineering; to be able to use theoretical and applied information in these areas to model and solve Bioengineering problems.

2

To be able to use scientific methods to complete and apply information from uncertain, limited or incomplete data; to be able to combine and use information from related disciplines.

3

To be able to design and apply theoretical, experimental and model-based research; to be able to solve complex problems in such processes.

4

Being able to utilize Natural Sciences and Bioengineering principles to design systems, devices and processes.

5

To be able to follow and apply new developments and technologies in the field of Bioengineering.

6

To be able to work effectively in multi-disciplinary teams within the discipline of Bioengineering; to be able to exhibit individual work.

7

To be able to have the knowledge about the social, environmental, health, security and law implications of Bioengineering applications, to be able to have the knowledge to manage projects and business applications, and to be able to be aware of their limitations in professional life.

8

To be able to have the social, scientific and ethical values ​​in the stages of collection, interpretation, dissemination and application of data related to the field of Bioengineering.

9

To be able to prepare an original thesis/term project in accordance with the criteria related to the field of Bioengineering.

10

To be able to follow information about Bioengineering in a foreign language and to be able to participate in discussions in academic environments.

11

To be able to improve the acquired knowledge, skills and qualifications for social and universal purposes regarding the studied area.

12

To be able to recognize regional and global issues/problems, and to be able to develop solutions based on research and scientific evidence related to Bioengineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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