Computer Vision is an important field of Artificial Intelligence concerned with automatically extracting useful information from images or videos. The explosive growth in digital imaging technologies, vision datasets and deep learning makes the problems of automated image interpretation even more exciting and more relevant than ever. This course introduces students to fundamental problems in computer vision and foundational techniques for solving them. 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, object recognition, deep learning and neural networks. The course features extensive practical components including computer labs that provide students with the opportunity to practice and refine their skills in computer vision.

Learning Outcomes

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

1. Understand and proficiently apply specialised knowledge, methods and skills in image processing and computer vision applications, research and development.
2. Identify, formulate and innnovatively solve problems in image processing and computer vision.
3. Critically analyse, evaluate and examine existing practical computer vision systems.
4. Design and develop practical and innovative image processing and computer vision applications or systems using advanced knowledge and research techniques.
5. Communicate effectively to both specialist and non-specialist audiences, explaining and synthesising complex concepts related to visual information processing systems.
6. Conduct themselves professionally and responsibly in the areas of computer vision, image processing and deep learning.
7. Critically read, review and assess scientific literature in the field and apply theoretical knowledge to identify the novelty and practicality of proposed methods.

Research-Led Teaching

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

Examination Material or equipment

A single double-sided A4 page (handwritten or printed) containing notes made by the student may be brought into the examination hall.

Recommended Resources

Computer Vision: Algorithms and Applications 2e, Richard Szeliski, Springer, 2022 (main text)

Multiple View Geometry in Computer Vision 2e, Richard Hartley and Andrew Zisserman, Cambridge University Press, 2004 (3D vision)

Other texts that may be useful:

Foundations of Computer Vision, Antonio Torralba, Phillip Isola and William T. Freeman, 2024 (recent text with an excellent treatment of most topics taught in the course)

Digital Image Processing 4e, Gonzalez and Woods, 2018 (particularly strong on histograms and image processing)

Computer Vision: A Modern Approach, Forsyth and Ponce, 2002

Pattern Recognition and Machine Learning, Christopher Bishop (broader reading, a more 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

Staff Feedback

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

• written comments: as summative feedback on assessment items
• verbal comments: within tutorial groups or in an individual convener/tutor appointment
• written and verbal to: whole class within lecture slide-packs, lab/tutorial 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.

Other Information

Workload

130 hours of student learning time across the semester includes:

• 4 hours scheduled time each week (a 2-hour lecture, a 1-hour lecture, and a 1-hour lab) for 12 weeks, as well as drop-in sessions.
• Students are expected to spend an average of 5-6 hours per week outside of the scheduled labs on tasks including practice exercises, reading, online discussion, assignment/project work, and exam preparation.

ChatGPT

The use of Generative AI Tools (e.g., ChatGPT) is permitted in this course, given that proper citation and prompts are provided, along with a description of how the tool contributed to the assignment. Guidelines regarding appropriate citation and use can be found on the ANU library website. Marks will reflect the contribution of the student rather than the contribution of the tools. Further guidance on appropriate use should be directed to the convener for this course.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction to computer vision and image formation	1) Book your computer lab time slot.2) Labs/Tutorials and Drop-Ins* Students are expected to participate in the tutorials and labs in which they enrolled.* Drop-Ins are open to all students in the cohort.
2	Low-level vision: image formation, representation and processing	Lab 0
3	Low-level vision: image filtering; mid-level vision: edge detection, image features	Lab 1; Lab 0 due
4	Mid-level vision: image features; high-level vision: introduction	Lab 2; Lab 1 due
5	High-level vision: deep neural networks	Lab 3; Lab 2 due
6	High-level vision: deep neural networks	Mid-term Test: online test assessing your learning of the course content up to this point in the semester; Work on mini-project assignment; Lab 3 due
7	3D vision: introduction, camera model, single-view geometry	Work on mini-project assignment
8	3D vision: camera calibration, two-view geometry (homography)	Lab 4; Mini-project assignment due
9	3D vision: two-view geometry (epipolar geometry, triangulation, stereo)	Lab 5; Lab 4 due
10	3D vision: multiple-view geometry; mid-level vision: optical flow, shape-from-X	Lab 6; Lab 5 due
11	Mid/High-level vision: self-supervised learning, detection, segmentation	Lab 6 due
12	Course review	Final exam during the exam period

Tutorial Registration

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

Assessment Summary

Assessment task	Value	Due Date	Learning Outcomes
Lab 1 Assignment	2 %	11/03/2025	1, 2, 5, 6
Lab 2 Assignment	2 %	18/03/2025	1, 2, 5, 6
Lab 3 Assignment	2 %	25/03/2025	1, 2, 5, 6
Lab 4 Assignment	3 %	29/04/2025	1, 2, 5, 6
Lab 5 Assignment	3 %	06/05/2025	1, 2, 5, 6
Lab 6 Assignment	3 %	13/05/2025	1, 2, 5, 6
Mid-Term Test	15 %	27/03/2025	1, 2, 3, 5
Mini-Project Assignment	15 %	24/04/2025	1, 2, 4, 5, 6, 7
Final Exam	55 %	*	1, 2, 3, 5

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

Policies

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:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

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.

Participation

You are encouraged to participate proactively in all the course activities, including lectures, labs, and the Ed forum.

Examination(s)

Formal examination.

Assessment Task 1

Lab 1 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 2

Lab 2 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 3

Lab 3 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 4

Lab 4 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 5

Lab 5 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 6

Lab 6 Assignment

Hands-on lab assignments covering key concepts learned in the lectures (6 assignments worth 2-3 marks each). The student responses will be assessed based on a set of pre-defined test cases and feedback will be predominantly auto-generated. Lab assignments are released on Mondays and are due on Tuesday of the following week. For example, the week 4 lab will be due in week 5 (Tuesday 23:59). The assignments will include coding questions on image processing, filtering, transformations, features, camera calibration, homography estimation and/or image warping.

Assessment Task 7

Mid-Term Test

An online test assessing your learning of the first half of the course content. This test must be completed individually and no materials are permitted, other than those listed below. The test is in the form of randomised multiple-choice questions on Wattle. It will not be invigilated and will not be held in the computer labs; it can be taken at any location. Once you start the test, you will have one hour to complete it, with only one attempt allowed. The goal of this test is for you to self-assess your understanding and to help prepare you for the difficulty level of the final exam.

Permitted materials:

1. A single double-sided A4 page (handwritten or printed) containing notes made by the student

2. Scribble paper

3. Calculator

4. Dictionary

Assessment Task 8

Mini-Project Assignment

A concise individual project report outlining the task addressed by the student, the proposed method, the results, and a reflection. Students will receive an open-ended prompt and (in some cases) data, and will solve their task using techniques from deep learning and computer vision. Submission guidelines and rubric will be shared on Wattle.

Assessment Task 9

Final Exam

An individual exam covering the topics presented across the entire course, unless otherwise stated. It will be held during the University's exam period.

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 submit all your assignments on Wattle or EdStem.

Hardcopy Submission

None. All assessment submissions are electronic through Wattle or EdStem.

Late Submission

Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

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. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students