Study programme 2023-2024Français
Computer Vision
Programme component of Master's in Electrical Engineering (MONS) (day schedule) à la Faculty of Engineering

CodeTypeHead of UE Department’s
contact details
Teacher(s)
UI-M1-IRELEC-371-MCompulsory UEGOSSELIN BernardF105 - Information, Signal et Intelligence artificielle
  • GOSSELIN Bernard
  • MANCAS Matei

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais
Anglais, Français162000033.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-ISIA-005Computer Vision1620000Q2100.00%

Programme component

Objectives of Programme's Learning Outcomes

  • Imagine, implement and operate systems/solutions/software to deal with a complex problem in the field of electricity by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
    • Identify complex problems to be solved and formulate the specifications by integrating client needs, contexts and issues (technical, economic, societal, ethical and environmental).
    • On the basis of modelling and experimentation, design one or more systems / one or more solutions / one or more software and/or hardware implementations responding to the problem posed; evaluate them taking into account the various parameters of the specifications.
    • Implement a chosen system/solution/software/circuit in the form of a diagram, flow chart, algorithm, plan, model, prototype, program, software and/or digital model.
    • Evaluate the approach and results for their adaptation (tests, measurements, optimisation and quality).
  • Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out electrical engineering missions, using their expertise and adaptability.
    • Master and mobilise knowledge, models, methods and techniques relating to the basics of electricity, electronics, automation, signal analysis and processing, telecommunications; modern electrical network engineering (production, transport, distribution); electric vehicles; advanced electronic systems; wired and wireless telecommunications; intelligent sensors; human-machine interfaces; mathematical modelling and analysis of dynamic systems; process control; image processing and processing; and the use of the Internet; advanced electronic systems; wired and wireless telecommunications; intelligent sensors; human-machine interfaces; mathematical modelling and analysis of dynamic systems; process control; image and sound processing and, more s
    • Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
    • Identify and discuss possible applications of new and emerging technologies in the field of electrical engineering.
    • Assess the validity of models and results in view of the state of science and characteristics of the problem.
  • Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
    • Argue to and persuade collaborators, clients, teachers and boards, both orally and in writing.

Learning Outcomes of UE

develop image processing techniques, together with a critical analysis of the problem;
apply image coding, analysis, segmentation and feature extraction techniques
apply classification and machine learning techniques (deep learning)

UE Content: description and pedagogical relevance

Image Processing, Image acquisition; lowlevel processing, filtering, transforms; image segmentation and registration;
Image Coding, Deep Learning

Prior Experience

fundamentals of signal processing; probability and statistics

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-ISIA-005
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
I-ISIA-005
  • Hybrid

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-ISIA-005Not applicable

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-ISIA-005Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-ISIA-005Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-ISIA-005Authorized

Term 2 Assessment - type

AAType(s) and mode(s) of Q2 assessment
I-ISIA-005
  • Oral examination - Face-to-face

Term 2 Assessment - comments

AATerm 2 Assessment - comments
I-ISIA-005Oral exam with written preparation time, without course material

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
I-ISIA-005
  • Oral examination - Face-to-face

Term 3 Assessment - comments

AATerm 3 Assessment - comments
I-ISIA-005Oral exam with written preparation time, without course material
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de dernière mise à jour de la fiche ECTS par l'enseignant : 09/05/2023
Date de dernière génération automatique de la page : 27/04/2024
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be