ITF31719 Image Processing (Spring 2022)

Facts about the course

ECTS Credits:
10
Responsible faculty:
Faculty of Computer Sciences, Engineering and Economics
Campus:
Halden
Course Leaders:
  • Sukalpa Chanda
  • Lars Vidar Magnusson
Teaching language:
Norwegian or English
Duration:
½ year

Table of contents

The course is affiliated to the following study programme

Compulsory course in

  • Bachelor in Computer Science - Machine Learning

Elective course in other study programmes.

Recommended previous knowledge

This course requires prerequisite knowledge in:
  • mathematics equivalent to the course in Mathematics for Computer Science / Discrete Mathematics
  • mathematics equivalent to the course in Mathematics 1 / Calculus
  • programming equivalent to the course in Object-oriented Programming / Programming 2

Teaching semester

6th semester (Spring).

Content

Representation methods for digital images, filtering, noise reduction, use of filters, detail enhancement, edge detection, image transformations, handling shape in an image, pattern recognition and classification and programming methods for image processing. Basics of Artificial Neural Networks, Support Vector Machines and Principle Component Analysis.

The student's learning outcomes on passing the course

Knowledge

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 filtered/processed for better quality
  • understands the principles and methods in basic pattern recognition

Skills

The student can

  • use standard filters to filter 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

Teaching and learning methods

Lectures, exercises and lab assignments/projects.

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

Workload

Approx. 250 hours.

4 hours of lectures per week, in addition to study groups and project work.

Exam

Individual portfolio assignment

Up to 6 assignments must be submitted by given deadlines and in accordance with specifications given 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

External and internal examiner, or to internal examiners.

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.

Reading list

The literature list was last updated January 9th, 2019.

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

Recommended literature:

Gonzalez, R. C og Woods, R. E: Digital Image Processing. Pearson. 4th edition

Last updated from FS (Felles studentsystem) Oct. 15, 2026 3:03:06 AM