Program Overview

Introduction to ITC369 Computer Vision

ITC369 Computer Vision is a subject that focuses on computer vision technology, including multiple view geometry and Recursive Bayesian estimation. The subject covers various topics, such as planar geometry, camera models, and epipolar geometry, as well as Kalman Filter and Particle Filter.

Subject Information

Grading System

The grading system for this subject is HD/FL.

Duration

The duration of this subject is one session.

School

This subject is offered by the School of Computing and Mathematics.

Assumed Knowledge

The assumed knowledge for this subject is MTH219.

Learning Outcomes

Upon successful completion of this subject, students should be able to:

• demonstrate understanding of multiple view geometry used in computer vision
• apply the theory of Recursive Bayesian estimation, including Kalman Filtering and Particle Filtering
• implement and analyse several algorithms using low-level computer vision library
• discuss and analyse several seminal algorithms in computer vision

Syllabus

This subject will cover the following topics:

Part 1: Multiple View Geometry in Computer Vision

• Planar geometry
• Camera models
• Camera parameter estimations
• Epipolar geometry and fundamental matrix

Part 2: Recursive Bayesian Estimation

• Probabilistic theories
• Kalman Filter
• Particle Filter

Subject Outlines and Offerings

No offerings have been identified for this subject in 2019. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.