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 (note that it can be adapted depending on the nature of the project): 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.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.2, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in biomedical engineering.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, II.3, III.2, III.3, III.4, IV.1, IV.2, IV.3, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.2, III.3, III.4, IV.1, IV.2, IV.8, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in electrical engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, II.3, III.2, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.
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
Face-to-face (no streaming, no podcasts)
- exercise sessions
- computer simulations
- a large project (which is compulsory) consisting in a software implementation of computer vision techniques applied to a real situation. The project is usually divided in sub-tasks.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
It includes a lecture on theory and training session per week.
The project must be delivered by the end of the first semester.
Course materials and recommended or required readings
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Additional information:
Written exam during the exam session (compulsory).
The exam is written and includes questions of theoretical nature and on the exercises. The exam is closed-book.
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!.
Important note !
If the project comprises several sub-tasks, failing to deliver the result of a sub-task means that this task AND all the following ones will be granted a note of 0 !
Final note computation weights:
- January: project = 2/3, written exam = 1/3
- August: project (partial note identical to that of January) = 1/2, written exam = 1/2
Work placement(s)
Organisational remarks and main changes to the course
Please note that the course is taught in english!
Important note about the project.
It is possible to propose a personal project instead of the compulsory one.
The conditions are as follows:
- the personal project must be directly related to the course subject.
- the personal project may not overlap with another course and may not cover the subject matter of a master's thesis.
- the project must be approved at the beginning of the year by the course teachers. To do this, the student must submit a one-page text including: (1) the context, (2) a description of the work to be carried out, and (3) the expected results.
Contacts
Teacher : M. Van Droogenbroeck (04/366 26 93) Secretary : 04/ 366 26 91 Assistant : Renaud Vandeghen