ELEC5304: Semester 1, 2026

Units ELEC5304 Semester 1 2026 [Normal day]

Unit outline_

ELEC5304: Intelligent Visual Signal Understanding

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit of study introduces basic and advanced concepts and methodologies in image processing and computer vision. This course mainly focuses on image processing and analysis methods as well as intelligent systems for processing and understanding multidimensional signals such as images, which include basic topics like multidimensional signal processing fundamentals and advanced topics like visual feature extraction and image classification as well as their applications for face recognition and object/scene recognition. It mainly covers the following areas: multidimensional signal processing fundamentals, image enhancement in the spatial domain and frequency domain, edge processing and region processing, imaging geometry and 3D stereo vision, object recognition and face recognition.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Mathematics (e.g. probability and linear algebra) and programming skills (e.g. Matlab/Java/Python/C++)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Luping Zhou, luping.zhou@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Creative work	Group Project 1 Experimental report	20%	Week 07	n/a	AI allowed
Outcomes assessed:
Data analysis	Group project 2 Project report	30%	Week 10	Before the due date	AI allowed
Outcomes assessed:
Experimental design	Group Project 3 Project report	25%	Week 12	n/a	AI allowed
Outcomes assessed:
Presentation	Group Project 3 Presentation Project report	25%	Week 12	n/a	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction/multidimensional signal processing fundamentals (principles of camera systems for visual information acquisition and digitization)	Lecture (2 hr)
Week 01	Review lecture content and practice	Self-directed learning (6 hr)
Week 02	Mathematical preliminaries for multi-dimensional signal processing - 2D convolution and Z-transform	Lecture (2 hr)
	Mathematical preliminaries for multi-dimensional signal processing - 2D convolution and Z-transform	Practical (1 hr)
	Review lecture/tutorial content and practice	Self-directed learning (6 hr)
Week 03	Mathematical preliminaries for multi-dimensional signal processing - matrix manipulation	Lecture (2 hr)
	Mathematical preliminaries for multi-dimensional signal processing - matrix manipulation	Practical (1 hr)
	Review lecture/tutorial content and practice	Self-directed learning (6 hr)
Week 04	Mathematical preliminaries for multi-dimensional signal processing - machine learning	Lecture (2 hr)
	Mathematical preliminaries for multi-dimensional signal processing - machine learning	Practical (1 hr)
	Review lecture content and do assignments (Assignment 1 starts)	Self-directed learning (6 hr)
Week 05	Image restoration	Lecture (2 hr)
	Image restoration	Practical (1 hr)
	Review lecture/tutorial content, practice, and do assignments	Self-directed learning (6 hr)
Week 06	Image enhancement	Lecture (2 hr)
	Image enhancement	Practical (1 hr)
	Review lecture/tutorial content, practice, and do assignments	Self-directed learning (6 hr)
Week 07	Image analysis - edge detection	Lecture (2 hr)
	Image analysis - edge detection	Practical (1 hr)
	Review lecture content, practice, and do assignments (Assignment 2 starts)	Self-directed learning (6 hr)
Week 08	Image analysis - segmentation	Lecture (2 hr)
	Image analysis - segmentation	Practical (1 hr)
	Review lecture/tutorial content, practice, and do assignments	Self-directed learning (6 hr)
Week 09	Introducing machine learning	Lecture (2 hr)
	Introduction to Machine learning software	Practical (1 hr)
	Review lecture content and do assignments (Assignment 3 starts)	Self-directed learning (6 hr)
Week 10	Basic information about deep learning	Lecture (2 hr)
	Basic information about deep learning	Practical (1 hr)
	Review lecture content, practice, and do assignments	Self-directed learning (6 hr)
Week 11	Application of deep learning for multidimensional signal processing - part 1	Lecture (2 hr)
	Application of deep learning for multidimensional signal processing - part 1	Practical (1 hr)
	Review lecture content, practice, and do assignments	Self-directed learning (6 hr)
Week 12	Application of deep learning for multidimensional signal processing - part 2	Lecture (2 hr)
	Application of deep learning for multidimensional signal processing - part 2	Practical (1 hr)
	Review lecture content, practice, and do assignments	Self-directed learning (6 hr)
Week 13	Review of the final project and presentations	Lecture (2 hr)
	Review of the final project and presentations	Practical (1 hr)
	Review lecture content and do assignments	Self-directed learning (6 hr)

Attendance and class requirements

There are no other requirements for this unit.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library eReserve link available on Canvas.

Digital Image Processing. R.C. Gonzalez and R.E. Woods. 3rd Edition. Prentice Hall. 2008.
Pattern Classification. R. Duda, P. Hart and D. Stork. 2nd Edition. Wiley. 2000.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. report results in a professional manner
LO2. use appropriate software platforms and tools for a given image processing or computer vision task
LO3. use the existing image processing and computer vision packages
LO4. apply the image processing and computer vision techniques to solve real world applications
LO5. understand the fundamental theory of image processing and computer vision algorithms.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Offer more course content on machine learning and deep learning.

Additional information

More information can be found on Canvas.

Additional costs

There are no additional costs for this unit.

Site visit guidelines

There are no site visit guidelines for this unit.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Disclaimer

On this page

Useful links