Computer Vision and Image Analysis (11376.2)
Please note these are the 2024 details for this unit
Available teaching periods | Delivery mode | Location |
---|---|---|
View teaching periods | On-Campus |
Bruce, Canberra |
EFTSL | Credit points | Faculty |
0.125 | 3 | Faculty Of Science And Technology |
Discipline | Study level | HECS Bands |
Academic Program Area - Technology | Level 3 - Undergraduate Advanced Unit | Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021) |
The aim of this research-led unit is to provide students with an overview of fundamental areas of computer vision and image analysis as well as in-depth knowledge of selected research topics, which will be explored in theory and practice.
Through specific, applied examples, students will explore this highly topical field in the context of a widespread use of digital camera and image technology. These include examples from application areas such as digital photography, digital image enhancement, computer graphics, object recognition, object tracking, image segmentation, visual motion estimation, and multi-view camera systems.
As computer vision and image analysis draw from other related fields, such as perceptual psychology, digital signal processing, human-computer interaction, artificial intelligence and pattern recognition, students will be able to explore relevant theories and algorithms in these areas.
This unit will be co-taught with unit 8890 Computer Vision and Image Analysis PG.
1. Have hands-on experience in what computer vision and image analysis entails and how images are formed and represented;
2. Understand the basics of image processing and analysis techniques;
3. Understand the concepts of fundamental theories in computer vision;
4. Write Matlab programs for performing fundamental computer vision and image analysis tasks;
5. Select appropriate computer vision and image analysis techniques to solve real-world problems; and
6. Understand the relationships between computer vision and image analysis on the one hand and fields such as perceptual psychology, digital signal processing, artificial intelligence and pattern recognition on the other hand.
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - think globally about issues in their profession
2. UC graduates are global citizens - understand issues in their profession from the perspective of other cultures
Through specific, applied examples, students will explore this highly topical field in the context of a widespread use of digital camera and image technology. These include examples from application areas such as digital photography, digital image enhancement, computer graphics, object recognition, object tracking, image segmentation, visual motion estimation, and multi-view camera systems.
As computer vision and image analysis draw from other related fields, such as perceptual psychology, digital signal processing, human-computer interaction, artificial intelligence and pattern recognition, students will be able to explore relevant theories and algorithms in these areas.
This unit will be co-taught with unit 8890 Computer Vision and Image Analysis PG.
Learning outcomes
Upon successful completion of this unit, students will be able to:1. Have hands-on experience in what computer vision and image analysis entails and how images are formed and represented;
2. Understand the basics of image processing and analysis techniques;
3. Understand the concepts of fundamental theories in computer vision;
4. Write Matlab programs for performing fundamental computer vision and image analysis tasks;
5. Select appropriate computer vision and image analysis techniques to solve real-world problems; and
6. Understand the relationships between computer vision and image analysis on the one hand and fields such as perceptual psychology, digital signal processing, artificial intelligence and pattern recognition on the other hand.
Graduate attributes
1. UC graduates are professional - communicate effectively1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
2. UC graduates are global citizens - think globally about issues in their profession
2. UC graduates are global citizens - understand issues in their profession from the perspective of other cultures
Prerequisites
11482 Pattern Recognition and Machine LearningCorequisites
None.Incompatible units
8890 Computer Vision and Image Analysis PGEquivalent units
None.Assumed knowledge
Working knowledge of discrete mathematics, algebra and numerical analysis.
Availability for enrolment in 2024 is subject to change and may not be confirmed until closer to the teaching start date.
Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
---|---|---|---|---|---|
2024 | Bruce, Canberra | Semester 1 | 05 February 2024 | On-Campus | Dr Roland Goecke |
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit
timetable.