Image Processing with Applications to Neural Networks
Course Code
Semester
Course Name
LE/RC/LA
Course Type
Language of Instruction
ECTS
EE0835
Image Processing with Applications to Neural Networks
2/2/0
DE
English
6
Course Goals
At the end of this course the student should be able to read understand books and papers written on image processing and use MATLAB to apply image processing operations such as 2-D FFT, filtering, image enhancement, histogram equalization, morphological operations to images in various formats such as jpeg, tiff etc. or constructed by themselves, describe the basics of image segmentation, image compression and motion detection.
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
-
Instructor(s)
Course Assistant(s)
Schedule
-
Office Hour(s)
Ofis 2C-02, Monday 13:00-15:00
Teaching Methods and Techniques
Lecture and Applications
Principle Sources
Prof.Dr. Vedat TAVŞANOĞLU (0).
Lecture Notes
Other Sources
-
Course Schedules
Week
Contents
Learning Methods
1. Week
Mathematical model of an image/ Separability in 2-D signals/The frequency concept in an image and its frequency spectrum/ Periodicity concept in an image /The Image Histogram.
2. Week
Expansion of an image into Fourier series, construction of an image from its harmonics.
3. Week
Digital Image: Sampling of an image, aliasing and conditions on sampling frequency.
4. Week
Classification of types of pixel operations applied to an image: Pixel-Point Operations such as lightening, darkening, changing the contrast (histogram enhancement); Pixel-Group Operations such as convolution operation and related concepts as the convolution mask and the impulse response.
5. Week
The 2-D Fourier transform, the 2-D Fourier tranform of separable images.
6. Week
The z-transform and the 2-D transfer function.
7. Week
2-D linear filtering:FIR filters: low-pass, high-pass, band-pass,band-stop filters
8. Week
2-D linear filtering:IIR Filters: Recursive computability and its conditions
9. Week
2-D nonlinear filtering: Median filters
10. Week
Methods of edge detection: (i)Gradian based methods: Sobel and Roberts edge detection methods; (ii) Laplacian based methods
11. Week
Morphological operations.
12. Week
The concept of image segmentation and compression
13. Week
Spatio-temporal filtering and its comparison with 2-D digital filtering
14. Week
Introduction of velocity-selective filtering and its relation to motion detection.
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Demonstrate an understanding of the digital image fundamentals including image capture system, representation, format and human vision perception.
LO-2
Apply image processing techniques in both spatial domain and spatial frequency domain to solve image processing problems.
LO-3
Compare different approaches to solving the same image processing tasks.
LO-4
Implement image processing algorithms on the computer by MATLAB.
LO-5
Distinguish appropriate image processing techniques to solve image processing problems.
