Image Processing and Computer Vision (ELE510)

Image processing is used in a growing number of applications in our daily lives as well as in research. Image processing is utilized for medical images, radar images, natural images, seismic data etc. in addition to robot vision. Thus, an understanding of classical image processing is useful in many fields.

Elements from both traditional image processing and computer vision are used to construct systems for robot (machine) vision. There is a rapid development in this field and applications are found in both industry and research. There are also many products with camera and software for processing of visual data. The objectives of this course are that the student should gain a fundamental understanding of Image Processing and Computer Vision with examples of applications.

Course description for study year 2023-2024. Please note that changes may occur.


Course code




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The course will consist of two parts with the following main content:

I: Image Processing

1. Introduction; Representation of digital images, color, light

2. Transformations and Spatial Filtering

3. Edge and corner detection

4. Feature extraction, textural features etc.

5. Image denoising

6. Image Segmentation

II: Computer Vision

1. Image Formation; Geometric camera models

2. Imaging with one camera

3. Multiple images; Stereopsis, Imaging with two cameras

4. Tracking

In addition, some topics on deep neural networks in image processing

Learning outcome


At the end of the semester a successful student should have knowledge about the following topics:

  1. Representation of digital images, including basic knowledge of light, shadow and color.
  2. Linear filtering of images. Linear filters can be used for noise removal, edge detection and analysis of image properties.
  3. Extracting features, for example texture features and corners from images
  4. Denoising of images
  5. Segmentation of images
  6. Principles of geometric camera models.
  7. Imaging with several cameras, especially stereo vision.
  8. Know the principles of segmentation, grouping and modeling in image processing and computer vision.


At the end of the semester a successful student should have skills in processing and analysis of digital images and be able to design simple robot vision systems and machine vision systems. The student should have skills in using python ( labs given as jupyter notebooks) and openCV for the purpose of image processing and image analysis.


After this course the student should have a general understanding of fundamental image processing and computer vision, where some concepts will be known at a more superficial level than others. The student should have a general understanding of how to use image processing and computer vision methods in real world applications.

Required prerequisite knowledge



Written exam and written report

Form of assessment Weight Duration Marks Aid
Written exam 6/10 4 Hours Letter grades Valid calculator
Written report 4/10 Letter grades All

The project will be done in groups of 2-3 persons. The report describes and documents the project work. Each group participant will get the same grade based on the report. If a student do not hand in the project report in time, it is considered to be a failed (i.e grade F), unless there is a valid absence of leave, which can give prolonged time.If a student/group fail the project, the student can do a new project with a different title next time the subject is run, or alternatively, in the retake-exam period in the following semester if agreed upon with the lecturer.

Coursework requirements

Laboratory work and mandatory exercises

Mandatory exercises: A set of mandatory exercises/laboratory work must be approved.

Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher within the specified deadlines.

Compulsory course attendance that must be completed and approved before access to the laboratory: Electronic Course in Health, Safety and Environment.

Completion of mandatory lab assignments are to be made at the times and in the groups that are assigned and published. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon.

Failure to complete the assigned labs on time or not having them approved will result in barring from taking the exam of the course.

The students must present the project work orally in order to get a grade in the subject.

Course teacher(s)

Course teacher:

Kjersti Engan

Coordinator laboratory exercises:

Luca Tomasetti

Coordinator laboratory exercises:

Saul Fuster Navarro

Course coordinator:

Kjersti Engan

Head of Department:

Tom Ryen

Method of work

4 hours lectures a week, including video lectures and Q&A classes instead of ordinary lectures that can be used on some larger or smaller parts of the material.

2 hours problem solving per week. Two of the weeks will be used for a project.

Overlapping courses

Course Reduction (SP)
Image Processing (MIK170_1) 10

Open for

Computer Science - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Robot Technology and Signal Processing - Master of Science Degree Programme Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto