CS7GV1 – Computer Vision

Module CodeCS7GV1
Module NameComputer Vision
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 1
Module Coordinator/s  Prof. Martin Alain

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Explain Perception, describe low to high level features;
  2. Understand and apply the mathematics involved in Computer Vision;
  3. Use programming languages (e.g. Matlab/Octave, Python) for developing applications with Computer Vision libraries and deep learning;
  4. Design Computer Vision modules as part of Intelligent Systems;
  5. Discuss critically (with a report, a demo and a presentation) a Computer Vision project.

Module Content

  • Image processing, feature detection and matching, image registration, recognition and segmentation;
  • Motion flow and object tracking in video;
  • Mathematics for Computer Vision.

Teaching and Learning Methods

Lectures, laboratories (programming) and online (e.g. discussion board).

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
AWeekly Short Quizzes for Week k=1 (first teaching week) to k=11 (except reading week k=7).L01/LO2/LO3/L
O5/LO4
5% for each (x10 = 50%)kk+1
BComputer Vision without deep
learning
LO2/LO3/LO420%Week 35/11 (week k=8)
CComputer Vision with deep
learning
L01/2/4/530%Week 1117/12

Reassessment Details

Coursework, 100%.

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:22 hours
Lecture22 hours
Independent Study (outside scheduled contact hours), broken down by:94 hours
Preparation for classes and review of material (including preparation for examination, if applicable)42 hours
Completion of assessments (including examination, if applicable)52 hours
Total Hours116 hours

Recommended Reading List

  • Computer Vision: A Modern Approach, Forsyth and Ponce, Pearson 2012 (available in TCD library).

Module Pre-requisites

Prerequisite modules: Mathematics (e.g. Linear algebra, Optimisation), statistics, machine learning, programming (e.g. Python), signal processing.

Other/alternative non-module prerequisites: Some computer programming experience.

Module Co-requisites

N/A

Module Website

Blackboard

Collaborate Ultra (TCD Only)