Study programme 2021-2022Français
Visual processing and smart spaces
Programme component of Master's in Computer Science à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCINFO-074-MOptional 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, Français242400044.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-ISIA-106Visual Processing and Smart Spaces2424000Q1100.00%
Programme component
Corequis

Objectives of Programme's Learning Outcomes

  • Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
    • Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
    • Demonstrate independence and their ability to work alone or in teams.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
    • Organise and lead a research, development or innovation project to completion.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
    • Where possible, communicate in a foreign language.
  • Apply scientific methodology.
    • Critically reflect on the impact of IT in general, and on the contribution to projects.

Learning Outcomes of UE

develop an applied image and/or video processing system, together with a critical analysis of the problem;
apply digital image analysis (low-level and high-level methods for denoising, segmentation,...) and video analysis and processing methods.

Content of UE

Image understanding and video processing Smart Spaces: Attention Analysis, Motion Capture, Interractions

Prior Experience

fundamentals of signal processing (sampling and quantization).

Type of Assessment for UE in Q1

  • Presentation and/or works
  • Oral examination

Q1 UE Assessment Comments

     1 Oral exam ,80%    1 Project Presentation ,20%
Evaluation methods may be adjusted according to the teaching/evaluation context imposed by health measures.

Type of Assessment for UE in Q3

  • Oral examination

Q3 UE Assessment Comments

oral examination, 100%
Evaluation methods may be adjusted according to the teaching/evaluation context imposed by health measures.

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-ISIA-106
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
I-ISIA-106
  • Mixed

Required Reading

AA
I-ISIA-106

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-ISIA-106Not applicable

Recommended Reading

AA
I-ISIA-106

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-ISIA-106Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-ISIA-106Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-ISIA-106Unauthorized
(*) 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 génération : 17/09/2021
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be