Skip to main content

EQ2330 Image and Video Processing 7.5 credits

Course memo Autumn 2021-1

Version 1 – 11/05/2021, 9:52:46 AM

Course offering

Autumn 2021-1 (Start date 01/11/2021, English)

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Course memo Autumn 2021-1

Course presentation

This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019

Content and learning outcomes

Course contents

This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.

Intended learning outcomes

After passing this course, participants should be able to

- describe and use the principles of digital image and video processing to develop image processing algorithms,

- develop image processing algorithms for image filtering and restoration, image transformation and multiresolution processing, image and video compression, as well as image matching and segmentation,

- implement (for example with MatLab) and assess the developed image processing algorithms, 

- explain algorithm design choices using the principles of digital image/video processing,

- develop image processing algorithms for a given practical image/video processing problem

- analyze given image/video processing problems, identify and explain the challenges, propose possible solutions, and explain the chosen algorithm design.

To achive higher grades, participants should also be able to

- solve more advanced problems in all areas mentioned above.

Learning activities

Individual preparation assignment, peer review, group projects, exercises, lectures.

Preparations before course start

Recommended prerequisites

EQ1220 Signal Theory or equivalent

Literature

No information inserted

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Assignment, 1.5 credits, Grading scale: P, F
  • TEN1 - Exam, 6.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

Preparation assignments, course projects, written examination.

Preparation assignments 1.5 ECTS (P/F). Course Projects 3 ECTS (A-F), Exam 3 ECTS (A-F). The final grade is the average of course projects and exam.

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

No information inserted

Course Coordinator

Teachers

Teacher Assistants

Examiner

Round Facts

Offered By

EECS/Intelligent Systems

Language Of Instruction

English

Course offering

Autumn 2021-1 (Start date 01/11/2021, English)

Contacts

Course Coordinator

Teachers

Teacher Assistants

Examiner