Skip to main content

We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

ELEC5304: Intelligent Visual Signal Understanding

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 object recognition, detection, segmentation etc. It mainly covers the following areas: multidimensional signal processing fundamentals, image enhancement in the spatial domain and frequency domain, edge processing and region processing, object recognition, detection, segmentation, and edge detection.

Details

Academic unit Electrical and Information Engineering
Unit code ELEC5304
Unit name Intelligent Visual Signal Understanding
Session, year
? 
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
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 and contact details

Coordinator Wanli Ouyang, wanli.ouyang@sydney.edu.au
Type Description Weight Due Length
Assignment group assignment Project 2
40% - n/a
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Assignment group assignment Paper reading presentation
A group of students presenting recent research works to others.
20% - 10 minutes per group
Outcomes assessed: LO1 LO4 LO5
Assignment Project 1
20% Week 08 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Recorded video presentation
Clarity of illustrating the key concept
20% Week 14 (STUVAC) 5 minutes for each student
Outcomes assessed: LO3 LO4 LO5
group assignment = group assignment ?

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 sydney.edu.au/students/guide-to-grades.

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Introduction/multidimensional signal processing fundamentals (principles of camera systems for visual information acquisition and digitization) Lecture (2 hr)  
Introduction/multidimensional signal processing fundamentals (principles of camera systems for visual information acquisition and digitization) Computer laboratory (1 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 Computer laboratory (1 hr)  
Week 03 Mathematical preliminaries for multi-dimensional signal processing - matrix manipulation Lecture (2 hr)  
Mathematical preliminaries for multi-dimensional signal processing - matrix manipulation Computer laboratory (1 hr)  
Week 04 Mathematical preliminaries for multi-dimensional signal processing - machine learning Lecture (2 hr)  
Mathematical preliminaries for multi-dimensional signal processing - machine learning Computer laboratory (1 hr)  
Week 05 Image restoration Lecture (2 hr)  
Image restoration Computer laboratory (1 hr)  
Week 06 Image enhancement Lecture (2 hr)  
Image enhancement Computer laboratory (1 hr)  
Week 07 Image analysis - edge detection Lecture (2 hr)  
Image analysis - edge detection Computer laboratory (1 hr)  
Week 08 Image analysis - segmentation Lecture (2 hr)  
Image analysis - segmentation Computer laboratory (1 hr)  
Week 09 Paper reading presentation Lecture (2 hr)  
Paper reading presentation Computer laboratory (1 hr)  
Week 10 Basic information about deep learning Lecture (2 hr)  
Basic information about deep learning Computer laboratory (1 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 Computer laboratory (1 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 Computer laboratory (1 hr)  
Week 13 Review and preparation for the exam Lecture (2 hr)  
Review and preparation for the exam Computer laboratory (1 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.

Prescribed 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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
Offe more course content on machine learning and deep learning.

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

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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