The main objectives of the course are as follows: To provide essential knowledge of image processing fundamentals. To develop advanced practical skills and competency in image processing. To apply these skills to the full spectrum of image processing applications, through independent research and investigation.
20
Contribution of the Course to Professional Development
21
Learning Outcomes:
1
Gain sufficient knowledge on image processing; the ability to model and solve computer vision application problems using theoretical and practical knowledge. ;
;
2
Gain the ability to identify, model, and solve complex problems; the ability to select and apply appropriate analysis and modeling methods for these problems. ;
;
22
Course Content:
Week
Theoretical
Practical
1
Introduction
2
Digital image fundamentals
3
Intensity Transformations
4
Histogram processing
5
Spatial Filtering
6
Smoothing Sharpening
7
Filtering in the Frequency
Domain
8
Filter design in frequency domain
9
Image Restoration and Reconstruction
10
Inverse filtering
11
Color image processing
12
Image compression
13
Morphological Image Processing
14
Image Segmentation
23
Textbooks, References and/or Other Materials:
Digital Image Processing, Rafael Gonzalez, 2nd edition Addison-Wesley
24
Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
0
0
Quiz
0
0
Homeworks, Performances
10
40
Final Exam
1
60
Total
11
100
Contribution of Term (Year) Learning Activities to Success Grade
40
Contribution of Final Exam to Success Grade
60
Total
100
Measurement and Evaluation Techniques Used in the Course
Information
25
ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
3
42
Practicals/Labs
0
0
0
Self Study and Preparation
0
0
0
Homeworks, Performances
10
12
120
Projects
0
0
0
Field Studies
0
0
0
Midtermexams
0
0
0
Others
0
0
0
Final Exams
1
18
18
Total WorkLoad
180
Total workload/ 30 hr
6
ECTS Credit of the Course
6
26
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
