CSCI935

Computer Vision Algorithms and Systems


Section A: Subject Information

Subject Description


This subject is designed to equip students with an understanding of fundamental concepts and tools required to analyse, design and implement practical computer vision systems. Topics covered include digital cameras, image enhancement, edge and shape detection, video processing and analysis, object (e.g. humans and faces) detection and recognition, and the recent advances. The subject will focus on practice of computer vision technologies using widely used libraries (e.g. openCV or Matlab Computer Vision toolbox).


Subject Learning Outcomes

On successful completion of this subject, students will be able to:

1. Understand the principle of digital image and video cameras.

2. Use image enhancement techniques.

3. Use object detection and recognition techniques.

4. Use video processing techniques to detect moving objects.

5. Design and implement basic computer vision systems for real applications.


Assessment Summary

  No.
  Assessment Name
  Assessment Weight
  Mapping to Subject Learning Outcome
  Task Due
  1
  Assignments
  60%
  SLO1, SLO2, SLO3, SLO4, SLO5
  To Be Announced Final submission time: 11:30pm
  2
  Final Exam
  40%
  SLO1, SLO2, SLO3, SLO4, SLO5
  UOW Exam Period

Detailed assessment information is available in Section B of the subject outline.


Student Workload

Students should note that UOW policy equates 1 credit point with 2 hours of study per week, including lectures and tutorials/workshops/practicals, self-directed study and work on assessment tasks. For example, in a 6 credit point subject, a total of 12 hours of study per week is expected.


Subject Changes and Response to Student Feedback

The School is committed to continual improvement in teaching and learning and takes into consideration student feedback from many sources. These sources include direct student feedback to tutors and lecturers, feedback through Student Services and the Faculty Central, and responses to the Subject Evaluation Surveys. This information is also used to inform comprehensive reviews of subjects and courses.


Extraordinary Changes to the Subject Outline

In extraordinary circumstances the provisions stipulated in this Subject Outline may require amendment after the Subject Outline has been distributed. All students enrolled in the subject must be notified and have the opportunity to provide feedback in relation to the proposed amendment, prior to the amendment being finalised.


Learning Analytics

"Where Learning Analytics data (such as student engagement with Moodle, access to recorded lectures, University Library usage, task marks, and use of SOLS) is available to the Subject Coordinator, this may be used to assist in analysing student engagement, and to identify and recommend support to students who may be at risk of failure. If you have questions about the kinds of data the University uses, how we collect it, and how we protect your privacy in the use of this data, please refer to https://www.uow.edu.au/about/privacy/index.html".


Your Privacy - Lecture Recording

In accordance with the Student Privacy & Disclosure Statement, when undertaking our normal teaching and learning activities, the University may collect your personal information. This collection may occur incidentally during the recording of lectures in equipped venues (i.e., when your identity can be ascertained by your image, voice or opinion), therefore the University further advises students that:

 Lecture recordings are made available to students, university staff, and affiliates, securely on the university's Echo360 ALP (Active Learning Platform) and via the subject Moodle eLearning site;

 Recordings are made available only for which they were recorded, for example, as a supplemental study tool or to support equity and access to educational resources;

 Recordings are stored securely for up to four years.

If you have any concerns about the use or accuracy of your personal information collected in a lecture recording, you may approach your Subject Coordinator to discuss your particular circumstances. The University is committed to ensuring your privacy is protected. If you have a concern about how your personal information is being used or managed please refer to the University's Privacy Policy or consult our Privacy webpage https://www.uow.edu.au/privacy/


Additional Information About This Subject

N/A


Subject eLearning

The University uses the eLearning system Moodle to support all coursework subjects. To access eLearning you must have a UOW user account name and password, and be enrolled in the subject. eLearning is accessed via SOLS (Student Online Service). Log on to SOLS and then click on the eLearning link in the menu column.

The University is committed to providing a safe, respectful, equitable and orderly environment for the University community, and expects each member of that community to behave responsibly and ethically. Students must comply with the University's Student Conduct Rules and related policies including the IT Acceptable Use Policy and Bullying Prevention Policy, whether undertaking their studies face-to-face, online or remotely. For more information on appropriate communication and etiquette in the online environment please refer to the guide Online and Email Etiquette.


Major Text

N/A


Recommended Readings

Students are encouraged to use the UOW Library catalogue and databases to locate additional resources including the e-readings list: https://ereadingsprd.uow.edu.au/


References

1. D. Forsyth, J. Ponce. Computer Vision a Modern Approach, Pearson, 2nd edition, 2011

2. E. R Davies, Computer and machine vision: theory, algorithms and practicalities, Academic Press; 4th edition; 2012

This is not an exhaustive list. Students are encouraged to use the UOW Library catalogue and databases to locate additional resources.


Other Resources

1. openCV online documentation and tutorials: https://opencv.org/


Additional Requirements / Materials to be Purchased

N/A


Lecture and Contact Hours

Current timetable information is located at https://www.uow.edu.au/student/timetables


Minimum Attendance Requirements

Satisfactory attendance is deemed by the University, to be attendance at approximately 80% of the allocated contact hours. Where attendance is affected due to compassionate, compelling, or extenuating circumstances an application for academic consideration application should be lodged. Failure to comply with mandatory minimum attendance requirements may constitute grounds for the award of a grade of Technical Fail (TF) in this subject.


Lecture Recordings

The University of Wollongong supports the recording of lectures as a supplemental study tool, to provide students with equity of access, and as a technology-enriched learning strategy to enhance the student experience.

If you make your own recording of a lecture you can only do so with the explicit permission of the lecturer and those people who are also being recorded. You may only use recorded lectures, whether they are your own or recorded by the university, for your own educational purposes. Recordings cannot be altered, shared or published on another platform, without permission of the University, and to do so may contravene the University's Copyright Policy, Privacy Policy, Intellectual Property Policy, IT Acceptable Use Policy and Student Conduct Rules. Unauthorised sharing of recordings may also involve a breach of law under the Copyright Act 1969.

Most lectures in this subject will be recorded, when they are scheduled in venues that are equipped with lecture recording technology, and made available via the subject Moodle site with 48 hours.


Lecture Schedule

This is a guide to the weekly lecture topics however the delivery date of these topics may on occasion vary due to unforeseen circumstances, such as the availability of a guest lecturer or access to other resources.

  Week Beginning
  Lecture Topics
  Tutorial/Workshop/Laboratory/Demonstration/Field Work
  Readings/Other subject information
  Task Due
  Week 1 26 Jul 2021 (Monday)
  Introduction
  N/A
  As advised in the lecture slides

  Week 2 02 Aug 2021 (Monday)
  Photometry Color & Image Acquistion
  N/A
  As advised in the lecture slides

  Week 3 09 Aug 2021 (Monday)
  Image Quality
  N/A
  As advised in the lecture slides

  Week 4 16 Aug 2021 (Monday)
  Edge Detection
  N/A
  As advised in the lecture slides
  Assignment 1
  Week 5 23 Aug 2021 (Monday)
  Keypoint Detection
  N/A
  As advised in the lecture slides

  Week 6 30 Aug 2021 (Monday)
  Shape Detection
  N/A
  As advised in the lecture slides

  Week 7 06 Sep 2021 (Monday)
  Image Segmentation
  N/A
  As advised in the lecture slides

  Week 8 13 Sep 2021 (Monday)
  Change Detection and Background Modeling
  N/A
  As advised in the lecture slides
  Assignment 2
  Week 9 20 Sep 2021 (Monday)
  Motion Estimation
  N/A
  As advised in the lecture slides

  27 Sep 2021
Mid-Session Recess
  Week 10 04 Oct 2021 (Monday)
  Object Detection
  N/A
  As advised in the lecture slides

  Week 11 11 Oct 2021 (Monday)
  Object Recognition
  N/A
  As advised in the lecture slides

  Week 12 18 Oct 2021 (Monday)
  Deep Leaning in Computer Vision
  N/A
  As advised in the lecture slides
  Assignment 3
  Week 13 25 Oct 2021 (Monday)
  Revision
  N/A


  01 Nov 2021
Study Recess
  08 Nov 2021
Examinations


Section B: Assessment

Minimum Performance Requirements

To be eligible for a Pass in this subject a student must achieve a mark of at least 40% in the final exam. Students who fail to achieve this minimum mark & would have otherwise passed may be given a TF (Technical Fail) for this subject.

Students who do not meet the minimum performance requirements, as specified for each assessment, will receive a TF (Technical Fail) grade for this subject, which will appear on your Academic Transcript.


Requirements Related to Student Contributions

Not applicable.


Referencing

Not applicable.

Please consult the UOW Library website for further information: https://uow.libguides.com/refcite


Detailed Assessment Information

Assessment 1
  Assessment Name
  Assignments
  Assessment Type
  Assignment
  Weighting
  60%
  Subject Learning Outcomes Assessed
  SLO1, SLO2, SLO3, SLO4, SLO5
  Individual or Group Assessment
  Individual
  Due Date
  To Be Announced
  Final submission time: 11:30pm
  Assessment Description and Criteria
  There are 3 assignments:
  Assignment 1 (15%) - due Week 4
  Assignment 2 (20%) due Week 8
  Assignment 3 (25%) due Week 12
  Criteria: Correctness, completeness and consistency with specifications
  Length / Duration
  N/A
  Method of Submission
  Online via Moodle
  Return of Assessed Work
  Marks and comments via subject's Moodle

Assessment 2
  Assessment Name
  Final Exam
  Assessment Type
  Exam
  Weighting
  40%
  Subject Learning Outcomes Assessed
  SLO1, SLO2, SLO3, SLO4, SLO5
  Individual or Group Assessment
  Individual
  Due Date
  UOW Exam Period
  Assessment Description and Criteria
  Correctness, completeness and consistency with specifications.
  Length / Duration
  2 hours
  Method of Submission

  Return of Assessed Work
  N/A


Academic Integrity

The University's policy on acknowledgement practice and plagiarism provides detailed information about how to acknowledge the work of others: https://www.uow.edu.au/about/policy/UOW058648.html

The University's Academic Integrity Policy, Faculty Handbooks and subject guides clearly set out the University's expectation that students submit only their own original work for assessment and avoid plagiarising the work of others or cheating. Re-using any of your own work (either in part or in full), which you have submitted previously for assessment, is not permitted without appropriate acknowledgement or without the explicit permission of the Subject Coordinator. Plagiarism can be detected and has led to students being expelled from the University.

The use by students of any website that provides access to essays or other assessment items (sometimes marketed as 'resources'), is extremely unwise. Students who provide an assessment item (or provide access to an assessment item) to others, either directly or indirectly (for example by uploading an assessment item to a website) are considered by the University to be intentionally or recklessly helping other students to cheat. Uploading an assessment task, subject outline or other course materials without express permission of the university is considered academic misconduct and students place themselves at risk of being expelled from the University.