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Computer Vision and Image Analysis (11376.1)

Level: Level 3 - Undergraduate Advanced Unit
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology

Availability

Unit Outlines

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  • Semester 1, 2020, ON-CAMPUS, BRUCE (192297) - View

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Syllabus

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.

Learning Outcomes

On successfully completing the unit, students will have a sound understanding of and will have gained hands-on experience in:

1. What computer vision and image analysis entails;

2. How images are formed and represented;

3. Understanding the basics of image processing and analysis techniques;

4. Understanding the concepts of fundamental theories in computer vision;

5. Writing Matlab programs for performing fundamental computer vision and image analysis tasks;

6. Being able to choose appropriate computer vision and image analysis techniques to solve real-world problems; and

7. Understanding 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.

Assessment Items

Contact Hours

Two hour lecture, one hour tutorial and one hour computer laboratory on-campus per week.

Prerequisites

Must have passed 48 credit points.

Corequisites

None.

Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

Incompatible Units

8890 Computer Vision and Image Analysis PG

Equivalent Units

None.



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