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Course Structure: The course material will be presented
through a combination of formal lectures, group readings and discussions,
homework-based labs, onsite visits and project work.
Learning Objectives: By the end of the course, students
will;
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Develop a solid understanding of digital
processing and analysis of higher dimensional signals.
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Learn the fundamentals of image capture
and acquisition.
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Understand how to represent high dimensional data in spatial and frequency domains.
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Perform low level image processing such as denoising, enhancement and restoration.
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Perform high level image analysis such as object localization and modeling.
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Compare and evaluate state-of-the-art techniques in multi-dimensional data computing.
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Appreciate the latest research developments in the related area.
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Develop some experience with real life practical applications.
Course Outline:
Fundamentals
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Elements of visual perception, image acquisition systems.
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Image representation, image coding.
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Image enhancement in the spatial and frequency domains.
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Color and morphological image processing.
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Image restoration.
Advanced
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Feature extraction, object recognition.
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Shape representation and object modeling including;
- Explicit and implicit models.
- Statistical models.
- Medial representations.
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Image segmentation including:
- Deformable models.
- Graph based approaches.
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Image registration.
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Motion analysis.
Applications and practice
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Real life applications e.g. medical imaging, industrial automation, and multi-media processing.
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On-site visits.
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Invited speakers.
Evaluation scheme:
Students will be evaluated based on attendance and participation,
homework assignments, presentations and discussions of research papers,
and a term course project.
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