Focuses on computer vision methods and techniques in contemporary expert/intelligent systems. Students will learn various image pre-processing, feature extraction, segmentation and classification algorithms and apply them to solve real-world computer vision problems. This unit provides students with in-depth understanding of computer vision and artificial intelligence technologies behind contemporary expert/intelligent systems.
1. Introduction to image processing and computer vision
2. Image pre-processing techniques
3. Feature extraction methods
4. Object detection and classification
5. Image segmentation
6. Computer vision systems in smart environments
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
|On completion of this unit, students should be able to:|
|1||analyse and apply appropriate image pre-processing algorithms to a real-world image processing/pattern recognition problem.|
|2||develop and apply suitable feature extraction methods to characterise images.|
|3||implement appropriate object detection and classification techniques.|
|4||build image segmentation and classification solutions.|
On completion of this unit, students should be able to:
- analyse and apply appropriate image pre-processing algorithms to a real-world image processing/pattern recognition problem.
- develop and apply suitable feature extraction methods to characterise images.
- implement appropriate object detection and classification techniques.
- build image segmentation and classification solutions.
Prescribed Learning Resources
- Prescribed text information is not currently available.
- Prescribed resources/equipment information is not currently available.
Teaching and assessment
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
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