Duration
30h Th, 10h Pr, 50h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Contents: introduction, linear filtering and deconvolution, mathematical morphology, non-linear filtering, features extraction and border detection, texture, enhancement and restoration, shape analysis, image segmentation, motion detection, aspects of 3D vision, machine learning, pattern recognition, deep learning
Learning outcomes of the learning unit
This course introduces to the major techniques used in computer vision. Theoretical and practical aspects of image processing are discussed in details, with a focus on industrial applications.
At the end of the course, students will be able to:
- master the notion of an image;
- understand the major vision processing techniques;
- design a complete video processing chain with a practical aim.
Prerequisite knowledge and skills
- The student shall have passed a course on advanced programming.
- The student shall be familiar with signal processing concepts.
Planned learning activities and teaching methods
Not face-to-face. One of these two options will be chosen: streaming or podcast.
Apart from the theoretical course, there are :
- exercise sessions
- computer simulations
- a large project (which is compulsory) consisting in a software implementation of computer vision techniques applied to a real situation
Mode of delivery (face to face, distance learning, hybrid learning)
It includes a lecture on theory and training session per week. The project must be delivered by the end of the first semester.
Organisational adjustments related to the current health context
Everything is steamed on the discord platform
Recommended or required readings
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
Any session :
- In-person
written exam ( open-ended questions )
- Remote
written exam ( open-ended questions )
- If evaluation in "hybrid"
preferred in-person
Additional information:
Written exam during the exam session (compulsory).
The exam is written and includes questions mainly of theoretical nature. The exam is closed-book.
If, for sanitray reasons, the University decide not to organize written exams, then the exam will be cancelled and the final note will be that of the homework.
Homework (compulsory).
This work must imperatively be given during the penultimate week of course of the first semester. Failure to achieve the required activities during the year will result in denying the possibility to pass the exam (1st AND 2d sessions!). There is no possibility to acheive the work during another semester than the one of the course (there is no second chance for the work).
Work placement(s)
Organizational remarks
Please note that the course is given in english!
Contacts
Teacher : M. Van Droogenbroeck (04/366 26 93) Secretary : 04/ 366 26 91 Assistant : Renaud Vandeghen