Digital Image Processing and Computer Vision, 7.5 credits

Learning outcomes

Knowledge and understanding
The student should after completing the course be able to

• explain theoretical concepts related to algorithms for digital image processing and computer vision,
• explain how information can be extracted from digital images,
• describe the use of computers in image processing and computer vision, and
• account practical applications for digital image processing and computer vision.

Skills and Abilities
The student should after completing the course be able to

• apply methods of image processing and computer vision and determine which methods are most suitable for a given problem, and
• report work performed in writing.

Making judgements and attitudes
The student should after completing the course be able to

• critically evaluate the suitability of different image processing and computer vision methods for different applications, and
• search for and evaluate scientific information related to image processing and computer vision.

Content

The following topics are addressed in the course

• Overview and introduction to image processing,
• image enhancement in the spatial domain,
• spatial filtering,
• filtering in the frequency domain,
• binary images and morphological operations,
• image segmentation,
• image descriptors,
• scene and object recognition in images, and
• practical application of methods for image processing in MATLAB.

Examinations and grades

Theory, 5.5 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Laboratory Excersices, 2 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

In order to obtain a passing grade for the entire course, a minimum grade of E is required for all the included theoretical parts and Passed for the practical parts.

Modes of assessment

• Theory (code A001): Written examination
• Laboratory Excercises (code A002): Written assignment

A retake will be scheduled to take place within eleven weeks of the regular examination.

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

Digital Signal Processing for MSc in Engineering, 6 Credits. The applicant must also have qualifications corresponding to the course "English B" or "English 6" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations.

Other provisions

The course may be given in English.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Obligatory literature
Gonzalez, Rafael and Woods, Richard E
Digital Image Processing (latest edition)
Pearson

Gonzalez, Rafael and Woods, Richard E
Digital Image Processing Using Matlab (latest edition)
Pearson

Szeliski, Richard
Computer Vision: Algorithms and Applications (latest edition)
Springer

E.R. Davies
Computer Vision: Principles, Algorithms, Applications, Learning (latest edition)
Academic Press