Course Catalog 2009-2010 Basic Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Serkan Kiranyaz, Moncef Gabbouj

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 1 : Monday 14 - 16, TB110 Per 2 : Monday 14 - 16, TB219	3.-n. vuosikurssi International Students

Requirements

Exercises and final exam.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

- Power point presentations with animations will be the primary tools used in the lectures - white board will also be used to illustrate the concepts - hands on are given during the exercise sessions - each student will carry out the exercises using mostly Matlab programming tools

Learning outcomes

This course is designed to help the student: • Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis. • Analyze and implement image processing algorithms. • Gain hands-on experience in using software tools for processing digital images.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	Fundamentals of digital image processing are discussed.	Sampling theorem and theory of quantization.	Nonlinear filtering.
2.	Image formation, sampling, quantization and the human visual system.	Fourier Transforms.	Image analysis and segmentation.
3.	Image enhancement in spatial and frequency domains.	Imputlse response and frequency response of Linear Filters.
4.	Image restoration in spatial and frequency domains.	Theory of transforms.
5.	Color spaces and color image processing.	Colorimetry.

Evaluation criteria for the course

Course Outcomes: This course requires the student to demonstrate the ability to: 1. Explain the basic elements and applications of image processing 2. Analyze image sampling and quantization requirements and implications 3. Perform Gray level transformations for Image enhancement 4. Apply histogram equalization for image enhancement 5. Use and implement order-statistics image enhancement methods 6. Design and implement two-dimensional spatial filters for image enhancement 7. Model the image restoration problem in both time and frequency domains 8. Explain Wiener filtering for de-blurring and noise removal 9. Explain the representation of colors in digital color images 10. Use Matlab to implement different image processing tasks 11. Document implementation code, report experimental results and draw proper conclusions 12. Prepare and submit a (optional) project report. Course assessment criteria: Grading is on a scale of 1-5. In order to pass the course, students must collect at least half the points from the final exam and attend at least 8 exercise sessions. A project work may also be assigned.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	"Digital Image Processing"	R. Gonzalez and R. Woods			http://www.imageprocessingplace.com/, 3rd edition, Prentice-Hall, New Jersey, 2008		English

Prerequisites

Additional information about prerequisites
Either SGN-1107 or SGN-1200 is required.

Correspondence of content

Additional information

This course is equivalent to SGN-3010

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1		Lectures Excercises Practical works Study journal, portfolio and other literary work	Contact teaching: 20 % Distance learning: 10 % Self-directed learning: 10 %

Last modified	16.11.2009
Modifier	Virpi Hämäläinen