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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

AMME4710: Computer Vision and Image Processing

This unit of study introduces students to vision sensors, computer vision analysis and digital image processing. This course will cover the following areas: fundamental principles of vision sensors such as physics laws, radiometry, CMOS/CDD imager architectures, colour reconstruction; the design of physics-based models for vision such as reflectance models, photometric invariants, radiometric calibration. This course will also present algorithms for video/image analysis, transmission and scene interpretation. Topics such as image enhancement, restoration, stereo correspondence, pattern recognition, object segmentation and motion analysis will be covered.


Academic unit Aerospace, Mechanical and Mechatronic
Unit code AMME4710
Unit name Computer Vision and Image Processing
Session, year
Semester 2, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

MTRX3700 OR MECH4720 OR MECH5720
Assumed knowledge

The unit assumes that students have strong skills in MATLAB.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Mitch Thomas James Bryson,
Lecturer(s) Mitch Thomas James Bryson ,
Tutor(s) Fred Westling ,
James Allworth,
Type Description Weight Due Length
Small continuous assessment Tutorial Activity: Week 1
Weekly tutorial activity
1.67% Week 01
Due date: 27 Aug 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Small continuous assessment Tutorial Activity: Week 2
Weekly tutorial activity
1.67% Week 02
Due date: 03 Sep 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Small continuous assessment Tutorial Activity: Week 3
Weekly tutorial activity
1.67% Week 03
Due date: 10 Sep 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Assignment Assignment 1
Assignment report and code, submitted via Turnitin and email
20% Week 04
Due date: 20 Sep 2020
approx. 12 hours, 8 page report and code
Outcomes assessed: LO3 LO4 LO5 LO7 LO8
Small continuous assessment Tutorial Activity: Week 5
Weekly tutorial activity
1.67% Week 05
Due date: 24 Sep 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Small continuous assessment Tutorial Activity: Week 6
Weekly tutorial activity
1.66% Week 06
Due date: 01 Oct 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Small continuous assessment Tutorial Activity: Week 7
Weekly tutorial activity
1.66% Week 07
Due date: 08 Oct 2020
2 hour session
Outcomes assessed: LO3 LO8 LO7 LO6 LO5 LO4
Assignment Assignment 2
Assignment report and code, submitted via Turnitin and email
20% Week 08
Due date: 25 Oct 2020
approx. 18 hours, 8 page report and code
Outcomes assessed: LO3 LO4 LO5 LO6 LO7 LO8
Assignment group assignment Major Project Proposal
Proposal for group major project
0% Week 09
Due date: 30 Oct 2020
1 page
Outcomes assessed: LO1 LO2
Presentation group assignment Project presentation
Group major project presentation
20% Week 12
Due date: 16 Nov 2020
10 minute presentation + video
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Major project final report
Group major project final report
30% Week 12
Due date: 20 Nov 2020
10 page report/paper plus code
Outcomes assessed: LO1 LO2 LO3 LO7 LO8
group assignment = group assignment ?
  • Tutorials: Students will work on specific tutorials during weeks 1, 2, 3, 5, 6 and 7 that will be marked. Tutorials will run during the remaining weeks and will be used to work on assignments and a major group design project.
  • Project presentation/final report: Students will spend the last 4 weeks of the course working in teams on a major design task. Teams will be assessed on their presentation and final design report. Presentation files are to be submitted Monday Week 12, with presentations to be held during lecture and tutorial time in Week 12. Final reports are due Friday Week 12.

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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 / Digital image fundamentals (5 hr) LO3 LO4
Week 02 Introduction to radiometry, colour, colour image processing, and projective geometry (5 hr) LO3 LO4
Week 03 Image filtering and edge detection (5 hr) LO4 LO5 LO8
Week 04 Image features, matching, correspondence, and detection (5 hr) LO3 LO5 LO7 LO8
Week 05 Stereo imaging, camera calibration, 2D/3D image projective relationships, image-based navigation (5 hr) LO3 LO5 LO6 LO7 LO8
Week 06 Image segmentation and clustering (5 hr) LO3 LO6 LO7 LO8
Week 07 Object recognition, image classification, introduction to machine learning (5 hr) LO3 LO6 LO7 LO8
Week 08 Image classification and deep learning in computer vision (5 hr) LO3 LO6 LO7 LO8
Week 09 Computer vision projects, software packages (5 hr) LO1 LO2 LO3 LO6 LO7
Week 10 Advanced image-based navigation, introduction to structure-from-motion, 3D image-based mapping (5 hr) LO1 LO2 LO3 LO6 LO7
Week 11 Advanced applications of computer vision: face detection and recognition (5 hr) LO1 LO2 LO3 LO6 LO7
Week 12 Computer vision research seminar (5 hr) LO1 LO2 LO3 LO6 LO7

Attendance and class requirements

Weekly tutorial tasks during weeks 1, 2, 3, 5, 6 and 7 require attendance and getting tutorial activity work marked off by the tutors during tutorial hours.

Attendance is required during lecture/tutorial time during week 12 as part of the group major project presentations.

Attendance during regular weekly lectures is highly recommended, but not assessed.

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

Useful resources in addition to the lecture notes:

  • D.A. Forsyth and J. Ponce, Computer Vision – A Modern Approach. Prentice Hall, 2003.
  • R. C. Gonzalez, R. E. Woods and S. L. Eddings, Digital Image Processing using Matlab. Pearson Prentice Hall, 2004.

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. demonstrate skills in presenting a final design solution to a computer vision/image processing problem
  • LO2. demonstrate skills in working on a design project within a team including communicating with team members, planning, and managing tasks
  • LO3. design an engineering solution to a given image processing task by selecting, developing and evaluating appropriate algorithms and techniques.
  • LO4. understand the fundamental principles of how images are formed including the basics of image sensors, radiometry, colour, and projective geometry
  • LO5. apply basic techniques in image processing including the use of image filtering, features, edge detection, colour spaces/transforms, and matching
  • LO6. apply advanced techniques in computer vision including stereo vision, 3D mapping, object detection, image classification, and use of machine learning algorithms in vision
  • LO7. apply a wide range of image processing techniques to real world applications
  • LO8. understand the type of algorithm required for a particular image processing task.

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
Feedback from students is an important part of the continuous improvement of this course. We have updated group project options for this semester amongst other changes in the delivery of the unit.


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

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