No offerings have been identified for this subject in 2016

ITC369 Computer Vision (8)


In this subject students learn computer vision technology in general and also focus on several specific algorithms. Students study the multiple view geometry in computer vision which includes topics on planar geometry, camera models, camera parameter estimations and epipolar and fundamental matrix. The students study the theory and practice of Recursive Bayesian estimation which includes topics on Kalman Filter and Particle Filter. The students also study other emerging topics in computer vision as computer vision is still an evolving technology.

+ Subject Availability Modes and Location

Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: ITC369
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Assumed Knowledge


Enrolment restrictions

Related subject(s)
MTH219 Linear algebra knowledge is required for understanding the mathematical models in computer vision

Learning Outcomes

Upon successful completion of this subject, students should:
* be able to demonstrate understanding of multiple view geometry used in computer vision including topics on planar geometry, camera models, camera parameter estimations and epipolar geometry with fundamental matrix;

* be able to apply the theory of Recursive Bayesian estimation, including Kalman Filtering and Particle Filtering in image based tracking problems;

* be able to implement and analyse several algorithms using low-level computer vision library;

* be able to discuss and analyse several seminal algorithms in computer vision


The 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


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.