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Computer Vision (COMP90086)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Krista A. Ehinger
Overview
Availability | Semester 2 - Dual-Delivery |
---|---|
Fees | Look up fees |
AIMS
From self-driving cars to automatic processing of medical scans, vision is a key sensory modality for a variety of artificial intelligence tasks. However, extracting meaning from images poses various computational challenges. In this subject, students will learn the basic principles of image formation and computational methods for interpreting images. Students will develop an understanding of the standard frameworks used in computer vision algorithms and their applications in tasks such as object recognition, target detection, and three-dimensional reconstruction. The programming language used is Python.
INDICATIVE CONTENT
Topics covered may include:
- Basics of image formation
- Illumination and reflectance models
- Colour spaces
- Feature detectors and descriptors
- Stereo correspondence
- Methods for recovering three-dimensional shape
- Image segmentation
- Categorical and instance-level recognition
Intended learning outcomes
On completion of this subject, students should be able to:
- Identify basic challenges involved in interpreting image data
- Understand and articulate the mathematical and/or algorithmic basis of common techniques used in computer vision
- Analyse, design, and implement relevant techniques in computer vision to given problems
- Undertake end-to-end research experiments, including evaluation with image datasets, including reporting results
- Communicate technical solutions to computer vision problems
Generic skills
- Ability to undertake problem identification, formulation, and solution
- Ability to utilise a systems approach to complex problems and to design and operational performance ability to manage information and documentation
- Capacity for creativity and innovation
- Ability to communicate effectively with both technical experts and the community at large
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP30027 | Machine Learning | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
COMP90049 | Introduction to Machine Learning |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
OR
Admission into the 100pt Program course entry point in the MC-IT Master of Information Technology
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Three individual programming assignments. Intended Learning Outcomes (ILO's) 3 and 4 are addressed in the individual assignments.
| From Week 3 to Week 10 | 20% |
Final, open-ended group research project, requiring 40-50 hours of work per student. ILO's 1, 3, 4 and 5 are addressed in the group research project.
| From Week 11 to Week 12 | 30% |
Final examination. ILO's 1 and 2 are addressed in the final examination.
| End of semester | 50% |
Last updated: 31 January 2024
Dates & times
- Semester 2
Principal coordinator Kris Ehinger Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours, comprising 2 hours of lecture and 1 hour of workshop per week Total time commitment 200 hours Teaching period 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Krista A. Ehinger
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Computer Science Course Master of Data Science Course Master of Information Technology Course Master of Engineering Specialisation (formal) Computing Specialisation (formal) Distributed Computing Specialisation (formal) Software with Business Specialisation (formal) Artificial Intelligence Specialisation (formal) Human-Computer Interaction Specialisation (formal) Software Specialisation (formal) Cyber Security Specialisation (formal) Mechatronics - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 31 January 2024