Study programme 2019-2020Français
Visual processing and smart spaces
Programme component of Master's in Computer Science à la Faculty of Science

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3

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
Anglais242400044.001st term

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

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 Examen(s) oral(aux) ,80%    1 Présentation(s) finale(s) de Projet/Travail personnel ,20%

Type of Assessment for UE in Q3

  • Oral examination

Q3 UE Assessment Comments

oral examination, 100%

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-TCTS-106
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur

Mode of delivery

AAMode of delivery
I-TCTS-106
  • Face to face

Required Reading

AA
I-TCTS-106

Required Learning Resources/Tools

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

Recommended Reading

AA
I-TCTS-106

Recommended Learning Resources/Tools

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

Other Recommended Reading

AAOther Recommended Reading
I-TCTS-106Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-TCTS-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 : 13/07/2020
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Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be