ECE5176 Computer vision
Faculty of Engineering
ECE5176 Computer vision is a level 5, 6-credit-point, postgraduate unit from the Faculty of Engineering, offered in 2023 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2023
- Semester 1
- Clayton
- Assessment
- No exam
- 3 tasks
- Workload
- 144 hours
- per semester
This is the 2023 handbook entry. See the 2027 entry.
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Requisites
Before ECE5176
No prerequisites or corequisites besides the enrolment rules below.
After ECE5176
No unit lists ECE5176 as a prerequisite in the 2023 handbook.
Enrolment rules
You must be currently enrolled in a master’s course in engineering or related STEM and in a related discipline. The unit assumes that you have the necessary foundational knowledge.
Overview
This unit aims to develop an understanding of methods for extracting useful information (eg 3-D structure; object size, motion, shape, location and identity, etc) from images. It will allow you to understand how to construct computer vision systems for robotics, surveillance, medical imaging, and related application areas.
Offerings in 2023
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Lab assessmentsThreshold hurdle32%
- QuizzesThreshold hurdle8%
- Final assessmentThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Appreciate different camera models.
- 2
Discuss the elements of the human visual system and perception.
- 3
Apply geometry and photometry to image analysis.
- 4
Generate implementations for low-level vision processes such as linear filtering, edge detection, texture, multi-view geometry, stereopsis, structure from motion and optical flow and mid-level vision processes, such as segmentation and clustering, model fitting and tracking.
- 5
Design and implement high-level vision processes such as model-based vision, surfaces and outlines, graphs, range data, templates and classifiers and learning methods.
- 6
Synthesise and design code to complete computer vision programming exercises in programming languages such as C and MatLab.
Workload and teaching
- Lectures12 hours
- Practical activities12 hours
- Laboratories24 hours
- Teaching approachEnquiry-based learning
- Teaching approachPeer assisted learning
- Teaching approachActive learning
- Teaching approachProblem-based learning
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
Active participation in laboratories.
Active participation in the Slack discussion forums (Clayton-only) and the practical sessions (Clayton and Malaysia).
Participation and problem-solving in the laboratories and practical sessions.
Active participation in the practicals.
Learning resources
Recommended resources
Szeliski, Richard, "Computer Vision: Algorithms and Applications" (2nd Edition).
The book is freely available by the author.
Contacts
- Unit Coordinators
- Associate Professor Mehrtash Tafazzoli Harandi
- Chief Examiners
- Associate Professor Mehrtash Tafazzoli Harandi
Common questions
What are the prerequisites for ECE5176?
ECE5176 has no prerequisites, but enrolment rules apply.
When is ECE5176 offered?
In 2023, ECE5176 runs in Semester 1 at Clayton.
How much work is ECE5176?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ECE5176 have an exam?
No. ECE5176 has 3 assessment tasks and no exam.