• Class Number 4409
  • Term Code 3230
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • AsPr Nick Barnes
    • AsPr Nick Barnes
  • Class Dates
  • Class Start Date 21/02/2022
  • Class End Date 27/05/2022
  • Census Date 31/03/2022
  • Last Date to Enrol 28/02/2022
    • Jiawei Liu
    • Jiyang Zheng
    • Sahir Shrestha
    • Taylor Qin
SELT Survey Results

Computer Vision is an important field of Artificial Intelligence concerned with questions such as "how to extract information from image or video, and how to build a machine to see". Recent explosive growth of digital imaging technology, advanced computing, and deep learning makes the problems of automated image interpretation even more exciting and much more relevant than ever. This course introduces students to fundamental problems in image processing and computer vision, as well as their state-of-the-art solutions. Topics covered in detail include: image formation, image filtering, camera geometry, thresholding and image segmentation, edge, point and feature detection, geometric frameworks for vision, single view and two views geometry; 3D visual reconstruction, camera calibration; stereo vision, image classification and object recognition, deep learning and neural networks for computer vision etc. The course features extensive practical components including computer labs and Term Research projects that provide students with the opportunity to practice and refine their skills in image processing and computer vision.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Understand and master basic knowledge, theories and methods in image processing and computer vision.
  2. Identify, formulate and solve problems in image processing and computer vision.
  3. Analyse, evaluate and examine existing practical computer vision systems.
  4. Communicate effectively and work in teams to develop a working computer vision system.
  5. Critically review and assess scientific literature in the field and apply theoretical knowledge to identify the novelty and practicality of proposed methods.
  6. Design and develop practical and innovative image processing and computer vision applications or systems.
  7. Conduct themselves professionally and responsibly in the areas of computer vision image processing and deep learning.

Research-Led Teaching

The students will learn the fundamentals underpinning current research in computer vision, and study some key papers and results.

Current research techniques will be introduced in Deep learning for Computer Vision. Students will implement some key computer vision algorithms in the labs.

Examination Material or equipment

Computer Vision: Algorithms and Applications, Book by Richard Szeliski (Main text)

Other texts that maybe useful:

Computer Vision: A Modern Approach, Forsyth and Ponce 2002

Multiple View Geometry in Computer vision, Book by Richard Hartley (3D vision)

Digital Image Processing, Now up to 4th Edition, by Gonzalez and Woods 2018. (Particularly strong on histograms and image processing)

Pattern Recognition and Machine Learning, Book by Christopher Bishop (broader reading, more the statistical approach)

Deep Learning resources:

Dive into Deep Learning — Dive into Deep Learning 0.16.6 documentation (d2l.ai), Zhang, Lipton, Li, Smola

Deep Learning Book by Goodfellow et al.

Python/NumPy Tutorial by Justin Johnson

PyTorch Tutorial

The Incredible PyTorch - PyTorch Tutorials, Projects, Libraries, Papers etc.

Mathematics for Machine Learning, Deisenroth, Faisal, Soon Ong, available at: https://mml-book.github.io/book/mml-book.pdf

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments (including, particularly ask your demonstrator in the Labs)
  • feedback to whole class, groups, individuals.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to Computer Vision
2 Image Formation, Representation and Processing
3 Image Filtering, Edge Detection and Filtering Labs begin, Start on Lab 1
4 Features and Segmentation
5 Face recognition / detection Lab 1 due; Lab 2 released
6 More recognition / detection, Possibly start 3D vision Midsemester quiz (week 6 or 7)
7 3D vision Lab 3; Assignment due
8 3D vision Lab 2 due; Lab 3 released
9 Shape from X and other 3D reconstruction techniques, Possibly start deep learning
10 Introduction to deep learning: Fundamentals, representation; backpropagation
11 Deep Learning for Computer Vision: Classification and architectures, detection, segmentation Lab 3 due
12 Complete deep learning, Revision Project presentations, project report; Final exam in exam period.

Tutorial Registration

See Wattle Page.

Assessment Summary

Assessment task Value Learning Outcomes
Laboratory exercises 50 % 1,2,3,4,5,6,7
Mid-Semester Test 15 % 1,2,3,5,7
Final Exam 35 % 1,2,3,5,7

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 50 %
Learning Outcomes: 1,2,3,4,5,6,7

Laboratory exercises

  1. There are three Labs that make up a total of 50% of the total assessment.
  2. The due dates are as follows:
  3. CLAB 1: Due Date: 2022-03-25 (worth 15%)
  4. CLAB 2: Due Date: 2022-04-29 (worth 15%)
  5. CLAB 3: Due Date: 2022-05-13 (worth 20%)
  6. CLABs are weighted significantly in the overall assessment. Please note that the date and time for submissions are based on Australian Eastern Standard Time (Canberra). This is particularly important for those students who are not in Australia. Marked CLABS will be returned within 3 weeks of the due date. Late submission for the Labs is permitted, please see guidelines below.
  7. Submission guidelines will accompany the Labs.
  8. Students are required to solve assignment assignments independently.

Assessment Task 2

Value: 15 %
Learning Outcomes: 1,2,3,5,7

Mid-Semester Test

Online exam that will cover the first approximately six weeks of lectures.

Assessment Task 3

Value: 35 %
Learning Outcomes: 1,2,3,5,7

Final Exam

Final examination worth 35% of the mark. This will cover all aspects of the course (unless otherwise stated).

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

AsPr Nick Barnes

Research Interests

Computer Vision, Deep Learning for Computer Vision, Saliency, Dense Prediction, 3D Vision

AsPr Nick Barnes

AsPr Nick Barnes

Research Interests

AsPr Nick Barnes

Jiawei Liu

Research Interests

Jiawei Liu

Jiyang Zheng

Research Interests

Jiyang Zheng

Sahir Shrestha

Research Interests

Sahir Shrestha

Taylor Qin

Research Interests

Taylor Qin

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions