FIT5221 Intelligent image and video analysis
Faculty of Information Technology
FIT5221 Intelligent image and video analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2027 in Semester 1 at Clayton. It needs FIT5215, FIT5047, FIT5197 or FIT5201.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Clayton
- Assessment
- Exam 40%
- and 3 other tasks
- Workload
- 144 hours
- per semester
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Requisites
Before FIT5221
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After FIT5221
No unit lists FIT5221 as a prerequisite in the 2027 handbook.
Enrolment rules
Prerequisite: For students enrolled in E3001, E3002, E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2004 AND FIT2086.
Prerequisite knowledge: Knowledge of data structures, programming in Python/Matlab and linear algebra
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit will discuss the fundamental and modern concepts in image and video analysis. You will be introduced to the basics of image processing and low-level vision. The topics related to image formation, operations, features and segmentation will enable you with the understanding for developing vision enabled systems. Concepts related to convolutional neural networks (CNN) will be introduced and recent examples will be thoroughly analysed. Recent computer vision concepts will be discussed from a deep learning perspective. The unit will be balance between the theoretical and the practical implementation aspects of computer vision.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | Blended |
Assessment
- Assignment 1Project20%
- Assignment 2Project20%
- Assignment 3Project20%
- Scheduled final assessment (2 hours and 10 minutes)Examination40%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
describe image analysis and low-level vision
- 2
describe semantic image and video understanding techniques
- 3
describe convolutional neural networks and their applications
- 4
implement and extend existing computer vision algorithms
- 5
evaluate and compare techniques suitable for adding vision capability to unimodal and multimodal intelligent systems
Workload and teaching
- Lectures24 hours
- Laboratories24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Where it fits
FIT5221 is part of 2 areas of study in the 2027 handbook.
Contacts
- Chief Examiners
- Dr Toan Do
Common questions
What are the prerequisites for FIT5221?
You need FIT5215, FIT5047, FIT5197 or FIT5201 before you enrol. Enrolment rules also apply.
When is FIT5221 offered?
In 2027, FIT5221 runs in Semester 1 at Clayton.
How much work is FIT5221?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5221 have an exam?
Yes. The exam is worth 40% of the final mark, alongside 3 other tasks.
Which majors and minors include FIT5221?
FIT5221 is part of Computational science and Software engineering.