ITD33517 Image Processing and Pattern Recognition (Spring 2019)

The course is connected to the following study programs

Elective course.

Recommended requirements

Knowledge equivalent to the courses Mathematics for Computer Science and Object-oriented Programming.

Lecture Semester

6st semester (Spring).

The student's learning outcomes after completing the course

Knowledge:

On completion of the course, the student:

• understands digital images and their properties
• is familiar with representation methods for digital images, including image coding and compression
• understands how images can be affected by noise
• understands how images can be filtered/processed for better quality
• understands the principles and methods in basic pattern recognition

Skills:

On completion of the course, the student can:

• use standard filters to improve image quality by filtering noise
• use standard filters to enhance the details in an image
• use standard techniques to detect edges, corners and objects in an image
• write programs for basic image processing and pattern recognition
• get a computer and/or computer system to recognize objects in an image

Content

Representation methods for digital images, image coding and compression, noise reduction techniques, use of filters, detail enhancement, edge detection, image transformations, handling shape in an image, pattern recognition and classification and programming methods for image processing.

Forms of teaching and learning

Lectures, exercises and lab assignments/projects.

If students from international partners attend courses, the lectures will be conducted in English.

Workload

Approx. 240 hours.

4 hours of lectures per week, as well as study groups and lab assignments/projects.

Examination

Individual portfolio assignment

6 assignments must be submitted by given deadlines and in accordance with specifications issued by the course instructor. An individual grade will be awarded on the basis of an overall assessment. 

Grading scale from A to F are used. 

Examiners

The exam is assessed by the course instructor and an internal or external examiner.

Conditions for resit/rescheduled exams

In the event of a resit or rescheduled examination, all parts of the portfolio must be re-submitted.

Course evaluation

This course is evaluated by a

• Mid-term evaluation (compulsory)

The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.

Literature

The literature list was last updated November 16, 2017

Web-based resources will be available on the learning platform.

Recommended literature:
Gonzalez, R. C and Woods, R. E: Digital Image Processing. Pearson. 3. edition. ISBN 978-0131687288.