Image Processing and Computer Vision (ELE510)

Content

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

Knowledge:

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.

Skills:

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.

Competence:

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

Exam

Coursework requirements

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:

Coordinator laboratory exercises:

Course coordinator:

Head of Department:

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.

Open for

Course assessment

Literature