Phelma Formation 2022

5PMSIVV3 : Image, Video and Computer Vision - WPMTIVV3

  • Number of hours

    • Lectures 6.0
    • Projects 0
    • Tutorials 6.0
    • Internship 0
    • Laboratory works 8.0

    ECTS

    ECTS 2.0

Goal(s)

This course provides a comprehensive introduction to image and video processing as well as computer vision. Major topics include image transformation and alignment, image compression, feature extraction and description using 2D and 3D descriptors, object recognition, geometry-based and physics-based vision and video analysis.

Contact Dawood AL CHANTI

Content(s)

  • CM Sessions:
    • Introduction to the course and its contents.
    • Evaluation.
    • 1st Lecture Feature Detectors and Descriptors for Object Recognition
  • CTD Sessions:
    • 2h CTD: Optical Flow Estimation
    • 2h CTD: 3D Reconstruction
    • 2h CTD: Object Tracking
    • 2h CTD: Video Compresion
  • BE Sessions:
    • 2h BE on Feature Extraction and Object Recognition
    • 2h BE on Optical Flow
    • 2h BE on 3D Reconstruction
    • 2h BE on Object Tracking in Video
    • 2h BE on Video Compression


Prerequisites
  • This course requires familiarity with:
    • linear algebra
    • calculus
    • basic probability
    • programming in Python

Test

  • Session 1:
    • QCMs: 3 QCMs, each weighing 10%, so total of 30%.
    • BE: BE report, will require implementing a significant computer vision algorithm : 50%.
    • Project: group based (2 members maximum), studies and presents a method we do not cover in class: 20%.
  • Session 2:
    • BE: Redo the entire BE with an in-depth analysis and a full report: 50%.
    • EXAM: Written exam 50%.


  • N1=50%BE + 50%QCMs
  • N2=50%BE + 50%EXAM

Additional Information

Course list
Curriculum->Master->Semester 9
Curriculum->Double-Diploma Engineer/Master->Semester 9

Bibliography

  • Computer Vision: Algorithms and Applications, 2nd ed. Richard Szeliski