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Course Outline


Contents

Course Details

Course Code COMP9517
Course Title Computer Vision
Units of Credit 6
Convenor Erik Meijering
Administration cs9517@cse.unsw.edu.au
Classes Timetable
Course Website http://www.cse.unsw.edu.au/~cs9517
Moodle Website https://moodle.telt.unsw.edu.au/course/view.php?id=70167
Lecture Recordings https://moodle.telt.unsw.edu.au/mod/lti/view.php?id=4812358
Handbook Entry Undergraduates https://www.handbook.unsw.edu.au/undergraduate/courses/2022/COMP9517
Handbook Entry Postgraduates https://www.handbook.unsw.edu.au/postgraduate/courses/2022/COMP9517
Student Representatives https://www.cse.unsw.edu.au/~stureps/

Course Summary

Computer vision is the interdisciplinary scientific field that develops theories and methods allowing computers to extract high-level information from digital images or videos. From an engineering perspective it seeks to automate perceptual tasks normally performed by the human visual system. Generally, vision is difficult because it is an inverse problem, where only insufficient information is available about the objects of interest in the image data. Physics-based mathematical and statistical models as well as machine-learning methods are used to assist in the task. Current real-world applications are wide-ranging, and include optical character recognition, machine inspection, object recognition in retail, 3D model building in photogrammetry, medical imaging, automotive safety, motion capture, surveillance, fingerprint recognition and biometrics, and others. This course provides an introduction to fundamental concepts and an opportunity to develop a real-world application of computer vision.

Assumed Knowledge

Before commencing this course you should:

  • Be able to program well in Python, or be willing to learn it independently.
  • Be familiar with data structures and algorithms, and basic statistics.
  • Be able to learn to use and integrate software packages, including OpenCV, Scikit-Learn, Keras.
  • Be familiar with vector calculus and linear algebra, or be willing to learn them independently.

Student Learning Outcomes

After completing this course you will be able to:

  • Explain basic scientific, statistical, and engineering approaches to computer vision.
  • Implement and test computer vision algorithms using existing software platforms.
  • Build larger computer vision applications by integrating software modules.
  • Interpret and comment on articles in the computer vision literature.

This course contributes to the development of the following graduate capabilities:

Graduate Capability Acquired In
Skills involved in scholarly enquiry Assignment, Project
In-depth engagement with relevant disciplinary knowledge in its interdisciplinary context Labs, Project
Capacity for analytical and critical thinking and for creative problem solving Labs, Project, Exam
Ability to engage in independent and reflective learning Assignment, Labs, Project
Skills to locate, evaluate, and use relevant information (information literacy) Project, Exam
Capacity for enterprise, initiative and creativity Project
Skills required for collaborative and multidisciplinary work Project
Skills of effective communication Project Demo & Report

Course Staff

There are various lecturers and administrators for this course. Details are shown below. Tutors will also be available for consultation via the forum and other online means.

Lecturer Email Role
Professor Erik Meijering
erik.meijering@unsw.edu.au Lecturer in Charge
A/Professor Yang Song yang.song1@unsw.edu.au Lecturer
Piumi Sandarenu cs9517@cse.unsw.edu.au Course Administrator
Dr Yanming Zhu
cs9517@cse.unsw.edu.au Course Administrator

Course Timetable

The course timetable is available here and via the main menu.

Consultation Schedule

The course will be run during 2 x 2-hour time slots per week (see Course Timetable above). The first time slot will be a 2-hour lecture and the second time slot will be a 1-hour lecture followed by a 1-hour consultation session. The latter is intended to provide information about the labs (in the first weeks of the course) and the project (in later weeks) and answer questions about these.

Teaching Rationale and Strategies

The principal mode of teaching is lecturing. Because of the volume of the material available on the subject, lectures are a better means to present high-level overviews as well as in-depth presentations of selected topics. The lectures are complemented by a programming assignment, lab sessions, and a group project. In summary, the course consists of:

  • Lectures: To learn about concepts and example applications.
  • Assignment: To learn solving a significant problem early and quickly.
  • Lab Sessions: To examine algorithms in more detail and provide an opportunity for evaluation and feedback.
  • Group Project: To learn working in a team and building a significant application.

Modes of Delivery

The course will be delivered entirely online using the following media:

  • Lectures: BlackBoard Collaborate (see Timetable for details)
  • Q/A Forum: Ed (asynchronous throughout the course)
  • Lab Consultations: BlackBoard Collaborate (synchronous on Fridays 11:00-12:00, Weeks 2-5, enter via Moodle login)
  • Project Consultations: BlackBoard Collaborate (synchronous on Fridays 11:00-12:00, Weeks 6-9, enter via Moodle login)
  • Project Demos: BlackBoard Collaborate (synchronous on Wednesday 16:00-18:00 and/or Friday 10:00-12:00, Week 10)

A roster will be made available so each student knows which tutor to go to for live consultations.

Lecture Schedule

Week Topic Lecturer
1 Introduction & Image Formation Professor Erik Meijering
2 Image Processing Professor Erik Meijering
3 Feature Representation A/Professor Yang Song
4 Pattern Recognition A/Professor Yang Song
5 Image Segmentation Professor Erik Meijering
6 Flexible Week (No Lectures)
7 Motion Tracking Professor Erik Meijering
8 Deep Learning Dr Tariq Khan and Dr Yanming Zhu
9 Applications Guest Speakers and Professor Erik Meijering (Coordinator)
10 Project Demos Professor Erik Meijering (Coordinator)

Student Assessment

Your performance in this course will be assessed as shown in the table below, which specifies planned release and due dates for all assessment components and their assigned marks. The mentioned release and due dates are indicative at this stage. Please refer to the course announcements around the indicated dates for more details.

Assessment Marks Release Due
Assignment 10% Week 2 Week 4
Lab Work 10% Weeks 2, 3, 4, 5 Weeks 3, 4, 5, 7
Group Project 40% Week 5 Week 10
Exam 40% Exam Period Exam Period

For the group project, qualitative feedback will be provided where applicable, and a group mark will be assigned at the end. Some form of peer assessment will be used to moderate marks assigned to each project member.

The group project is a significant part of the assessment for this course. A project group consists of 5 students working as a team to solve a given problem and build an application. To vary the group size, prior permission from the Lecturer in Charge is necessary.

Late Submission Penalty

Unless you have received special dispensation from the Lecturer in Charge, work that is submitted after the deadline during the term will incur a penalty of 5% of the maximum assessment mark per day, capped at five days (120 hours) from the assessment deadline, after which submission is closed. For the final exam, university exam rules will apply.

Resources for Students

This course will be held entirely online and all course materials will be provided online as well. There is no need to buy a book. In the lectures we will be referring to various resources for further reading, many of which are freely available online:

Other resources of interest (available from the library or perhaps online as well) include:

  • Linda G. Shapiro and George C. Stockman. Computer Vision. Prentice Hall, 2001.
  • Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing. Addison Wesley, 2008.
  • Milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing, Analysis and Machine Vision. Chapman and Hall, 2007.
  • Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. John Wiley and Sons, 2000.
  • Gérard Medioni and Sing Bing Kang. Emerging Topics in Computer Vision. Prentice Hall, 2005.

Course Evaluation & Development

This course is evaluated each term using the myExperience system. Based on student feedback in past terms, and the transition to online teaching, the following changes have been introduced:

  1. All course components, including the exam, will be entirely online.
  2. Lectures will be livestreamed and provide an opportunity to interact with the lecturer.
  3. Consultations will also be online and tutors will be encouraged to give informative examples.
  4. Feedback on the labs will be provided faster so it can be taken into account for later labs.
  5. Homework (not assessable) has been introduced to help prepare for the online exam.

Communication Etiquette

The Q/A Forum on the Ed platform is your first port of call (see link to the forum in the main menu). Please post any query there that may be of wider interest to fellow students, for example general questions on lectures, labs, project, assessments.

For more personal questions please contact (see Course Staff for contact details):

  • Lecturer in Charge for late submissions, absence, special considerations, deadlines, lab and assessment content.
  • Course Administration for issues with enrolment, file submission, group enrolment, or other admin matter.

While every effort will be made to respond quickly to queries, please allow 24 hours before posting or emailing reminders.

You are reminded to observe standards of equity and respect in dealing with students and staff, whether in person, by email, via forum posts, and any other means of communication (see Student Conduct below).

Special Consideration

If your work in this course is affected by unforeseen adverse circumstances, you should apply for Special Consideration. If your request is reasonable and your work has clearly been impacted, then:

  • For an assignment, you may be granted an extension.
  • For the final exam, you may be offered a supplementary exam.

Notice the use of the word "may". None of the above is guaranteed. It depends on your making a convincing case that the circumstances have clearly impacted your ability to work.

UNSW handles Special Consideration requests centrally (in the Student Lifecycle division), so all requests must be submitted via the UNSW Special Consideration website.

Special Consideration requests must be accompanied by documentation, which will be verified by Student Lifecycle. Do not contact the course convenor (Lecturer in Charge) directly about Special Consideration.

Extensions on the group project will be awarded only if the majority of the team is affected. You will get a mark calculated in the same way as other students who sat the original assessment.

If you cannot attend the final exam because of illness or misadventure, then you must submit a Special Consideration request, with documentation, through myUNSW within 24 hours of the exam. If your request is reasonable, you will be awarded a Supplementary Exam (also known as "Supp").

Notice that UNSW expects you to be available to sit Supplementary Exams if required. If you are awarded a Supp and do not attend, then your exam mark will be zero.

For further details on Special Consideration, see the UNSW Student website.

If you are registered with Disability Services, please forward your documentation to the Lecturer in Charge within the first two weeks of term.

Student Conduct

The Student Code of Conduct (see Information and Policy for more details) sets out what the University expects from students as members of the UNSW community. In addition to the learning, teaching, and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.

In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code of Conduct as discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy, or causing any person to fear for their personal safety, is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.

If you have any concerns, you may raise them with the Lecturer in Charge, or approach the School Ethics Officer or Grievance Officer or one of the student representatives.

Plagiarism Policy

Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:

Make sure you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible if your assignment files are not accessible by anyone but you. Where applicable, make sure to set the correct permissions in your CSE directory and code repository. Notice also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.

For the purposes of COMP9517, plagiarism includes copying or obtaining all, or a substantial part, of the material for your assignment, whether written or graphical report material or software code, without written acknowledgement in your assignment, from:

  1. A location on the internet (whether on the UNSW website or elsewhere).
  2. A book, article, or other document (whether published or unpublished) in whatever form.
  3. Another student (whether in your class or another class).
  4. Any other person (for example someone who writes assignments for money).

Notice that if you copy material from another student or non-student with acknowledgement, you will not be penalised for plagiarism, but the marks you get for this will be at the marker’s discretion.

The assessments provide opportunities for you to develop important skills. Use these opportunities!

Academic Integrity

UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity.

If you have not done so yet, please take the time to read the following in full:

The pages below describe the policies and procedures in more detail:

Important Health Information

Please refer to the following resources for information about Safe Return to Campus and Studying Guidelines.

Resource created Wednesday 07 September 2022, 05:06:08 PM, last modified Tuesday 27 September 2022, 03:30:08 PM.

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