School of Computer Science

Module 06-19339 (2017)

Computational Vision

Level 2/I

Hamid Dehghani Semester 1 10 credits
Co-ordinator: Hamid Dehghani
Reviewer: Ales Leonardis

The Module Description is a strict subset of this Syllabus Page.

Outline

The module provides an introduction to computer vision, intended for students with some prior background in AI. Appropriate computational models, techniques and algorithms will be introduced, so that students can both understand the relevant literature and construct simple software systems.


Aims

The aims of this module are to:

  • provide a general introduction to computer vision
  • give an overview of computational models of visual processing in animals
  • introduce a number of different frameworks and representations for vision
  • familiarise the student with a number of techniques and algorithms in computer vision
  • provide a foundation for further study in the area of computer vision

Learning Outcomes

On successful completion of this module, the student should be able to:

  1. make informed choices about which sort of algorithms to apply to solve specific problems
  2. use standard vision libraries or software to construct working vision systems
  3. apply algorithms to simplified problems by hand
  4. discuss the advantages and drawbacks of different methods, explaining their working

Restrictions

None


Teaching methods

2 hrs lectures per week, 1 hr lab/exercise class

Contact Hours:

Approx. 34


Assessment

Sessional: 1.5 hr examination (70%), continuous assessment (30%).

Supplementary (where allowed): By examination only.

The continuous assessment consists of a team project.


Detailed Syllabus

  1. Taught topics include (but are not limited to):
    • Introduction to Computational and Human Vision
    • Human Vision
    • Edge Detection
    • Noise Filtering
    • Hough Transform
    • ROC Analysis
    • Motion
    • Feature Detection
    • Cross Correlation
    • Face Recognition
    • Object Recognition
  2. Laboratory Excercises:
    • Matlab Tutorial
    • Edge Detection
    • Noise Filtering
    • Hough Transform
    • Eigenfaces

Programmes containing this module