Units / FIT5221
FIT5221 · Intelligent image and video analysis
2026 Handbook6 credit pointsLevel 5Faculty of Information Technology
Last checked: 23 Aug 2026 UTCOverview
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
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Activities scheduled as a mix of on-campus and online activities (BLENDED) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Assignment 1 | Project | 20% | Threshold |
| 2 | Assignment 2 | Project | 20% | Threshold |
| 3 | Assignment 3 | Project | 20% | Threshold |
| 4 | Scheduled final assessment (2 hours and 10 minutes) | Examination | 40% | Threshold |
This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
prohibitions
- ITO5221 — Intelligent image and video analysis
Learning outcomes
- describe image analysis and low-level vision
- describe semantic image and video understanding techniques
- describe convolutional neural networks and their applications
- implement and extend existing computer vision algorithms
- evaluate and compare techniques suitable for adding vision capability to unimodal and multimodal intelligent systems
Workload
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.
| Activity | Duration |
|---|---|
| Lectures | 24 hours |
| Laboratories | 24 hours |
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