COMPSCI 773 : Intelligent Vision Systems


2022 Semester One (1223) (15 POINTS)

Course Prescription

Computational methods and techniques for computer vision are applied to real-world problems such as 2/3D face biometrics, autonomous navigation, and vision-guided robotics based on 3D scene description. A particular feature of the course work is the emphasis on complete system design. Recommended preparation: COMPSCI 373 and 15 points at Stage II in Mathematics.

Course Overview

This course introduces computational methods and techniques for computer vision, extended towards real-world problems such as vision-guided robotics based on 3D scene description and features for learning (including deep-learning examples). The students will interact with 3D vision equipment during in-class practical sessions as well as in the city campus Intelligent Vision Systems Lab ( -class size and schedule permitting. A particular feature of the course work is the emphasis on complete system design. Practical in-class experiment sessions will be held during lecture time. Weekly quiz and/or programming assignments will support the course materials via online delivery.  In-class or online tests (two hours each) will be held in weeks 6 and  12.

Building upon COMPSCI 373, COMPSCI 773 delivers more in-depth  understanding of established and current computer vision methodology and research, which is valuable for honours, MSc and PhD projects. Furthermore, computer vision techniques is an area where specialists are sought for by industry (e.g. artificial intelligence, robotics, gaming or augmented reality), both nationally as well as internationally.

Course Requirements

Prerequisite: Approval of Academic Head or nominee

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Explain how fundamental computer vision methods like calibration, 3D reconstruction, image stitching, registration, and image understanding work from a theoretical (mathematical) point of view. (Capability 1, 2 and 5)
  2. Apply computer vision methods in practice to solve posed programming assignment problems. (Capability 1 and 3)
  3. Communicate, analyse and explain current computer vision methods and its criticisms to peers. (Capability 2 and 4)
  4. Apply geometric principles to solve spatial 3D problems. (Capability 1 and 3)
  5. Demonstrate knowledge of Python to solve practical computer vision problems. (Capability 3)


Assessment Type Percentage Classification
Assignments 60% Group & Individual Coursework
Theory 40% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3 4 5


For more information and to find contact details for the CompSci 773 Tuākana coordinator, please see

Key Topics

Computer vision, calibration, image features for learning and matching, registration, machine learning based methods

Special Requirements

No special requirements.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 2 hours of lectures, a 1 hour lecture/tutorial, 2 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including tutorials to complete components of the course.
Lectures will be available as recordings. Other learning activities including seminars/tutorials will be available as recordings and online resources.
The course will include live online events including group discussions/tutorials.
Attendance on campus is required for the in-class tests.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Recommended reading:
G Gimel'farb, P. Delmas; Image Processing and Analysis: A Primer, World Scientific Europe, ISBN 978-1-78634-581-3, 2018.
Chap.1 to Chap.5 and Chap.8
R. Hartley and A. Zisserman: Multiple View Geometry in Computer Vision. 2nd edition. Cambridge University Press: Cambridge, UK, 2006.
Part 0: Projective geometry, 2D/3D transformations.
Part I: Camera geometry and single view geometry.
Part II: Two-view geometry: Epipolar geometry, fundamental matrix, 3D reconstruction.
Z. Zhang: Determining the Epipolar Geometry and its Uncertainty: A Review. International Journal of Computer Vision. vol. 27, no. 2, 1998, pp. 161 - 198.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

The course has moved to partial online delivery of assignments (written and programming) as well as more supporting material available through coderunner.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 11/11/2021 11:11 a.m.