Course - Computer Vision - IDATG2206
Computer Vision
About
About the course
Course content
This course IDATG2206 provides an introduction to the fundamental concepts and techniques of image processing and computer vision, a rapidly growing field that enables computers to interpret and understand the visual world. Students will gain a comprehensive understanding of image formation, image processing, feature extraction, image compression, different application areas of computer vision. The course will also enable the students to explore and understand real-world applications of computer vision in various domains.
- Image formation and low level processing
- Image acquisition
- Camera and optics
- Light and color
- Color imaging
- Image filtering
- Morphological image processing
- Image enhancement and restoration
- Feature detection and matching
- Image segmentation
- Image registration
- Image and video compression
- Image quality
- Introduction to spectral imaging, basic workflow and processing
- Introduction to machine learning applications.
- Application areas of computer vision
The above mentions topics will be covered through lectures, lab sessions, assignments, and projects.
Learning outcome
Knowledge:
On successful completion of this course, students should have the knowledge to:
- Understand basic concepts, terminology, theories, and methods in the field of image processing and computer vision
- Describe basic methods of computer vision. -assess which methods to use for solving a given problem, and analyze the accuracy of the methods in image processing and computer vision.
Skills:
Upon completion of the course, the students will acquire skills to:
- Develop and apply image processing, computer vision techniques for solving practical problems - choose appropriate image processing methods for image filtering, image restoration, image reconstruction, segmentation, classification and representation.
- Able to design and implement algorithms for computer vision applications in different application areas
General competence:
- Apply knowledge and skills to new areas to understand and conduct complex tasks and projects.
- Analysis relevant professional and research problems.
Learning methods and activities
- Lectures
- Project
- Assignments
- Lab exercises
Compulsory assignments
- Oblig
Further on evaluation
Mandatory assignments and projects have to be completed in order to be eligible to appear for the main exam.
Submission of project report is mandatory.
Deadlines for assignments and project will be announced during the beginning of the semester
The final assessment will be based on an oral exam.
There will be a re-sit exam in August/September. Re-sit exam can be in the form of written or oral.
A project needs to be resubmitted next time the course is run.
Specific conditions
Admission to a programme of study is required:
Computer Science - Engineering (BIDATA)
Programming (BPROG)
Recommended previous knowledge
IMAG1001 Mathematical Methods 1 or BMA1021 Mathematics for Programming
Course materials
Book:
- Digital Image Processing by Rafael C. Gonzalez and Richard Eugene Woods
- Digital Image Processing Using MATLAB (DIPUM), by Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Pearson (2018).
Lecture notes and other supplementary material relevant to the course will be provided.
Subject areas
- Engineering