Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	YZM3008 - Introduction to Image Processing
Course Type	Required Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	5.0
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery	This course is offered exclusively in an in-person format.
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	There are no other recommended courses related to this one.
Required or Recommended Resources	Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.Burger, W., & Burge, M. J. (2022). Digital image processing: An algorithmic introduction (3rd ed.). Springer.Minichino, J., & Howse, J. (2015). Learning OpenCV 3 computer vision with Python (2nd ed.). Packt Publishing.Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer.Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2020). Digital image processing using MATLAB, Python, and C++. Gatesmark Publishing.
Recommended Reading List	Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.Burger, W., & Burge, M. J. (2022). Digital image processing: An algorithmic introduction (3rd ed.). Springer.Minichino, J., & Howse, J. (2015). Learning OpenCV 3 computer vision with Python (2nd ed.). Packt Publishing.Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer.Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2020). Digital image processing using MATLAB, Python, and C++. Gatesmark Publishing.
Assessment methods and criteria	1 midterm exam, 1 project, 1 final exam
Work Placement	This is not suitable for this class.
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Content

Weeks	Topics
Week - 1	Introduction to Digital Image Processing: Pixels, Resolution, Color Spaces (RGB, Grayscale, HSV).
Week - 2	Image Processing: Reading/Writing Images, Accessing pixels as NumPy arrays, ROI.
Week - 3	Point Operators: Brightness, Contrast Adjustment. Image Arithmetic.
Week - 4	Histograms: Calculating and plotting histograms. Histogram Equalization.
Week - 5	Spatial Filtering: The Concept and Math of Convolution.
Week - 6	Blurring Filters (Mean, Gaussian).
Week - 7	Noise Models (Salt-Pepper). Noise Reduction (Median Filter). Sharpening.
Week - 8	Edge Detection: Gradients and Derivatives. Sobel and Prewitt Operators.
Week - 9	The Laplacian Operator. The Canny Edge Detector.
Week - 10	Image Segmentation: Thresholding Methods (Simple, Global - Otsu's Method).
Week - 11	Adaptive Thresholding.
Week - 12	Morphological Operations: Erosion and Dilation.
Week - 13	Morphological Operations: Opening and Closing.
Week - 14	Feature Extraction. Color-Based Segmentation.

Learning Activities and Teaching Methods

Teaching Methods
Lecture
Question & Answer
Team/Group Work
Drill - Practise
Problem Solving
Brain Storming
Competences
Productive
Rational
Questoning
Use time effectively
Abstract analysis and synthesis
Problem solving
Information Management
Elementary computing skills
Decision making

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0