Postgraduate Course: Image Processing (PGEE11021)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Image Processing refers to the use of algorithms to analyse, modify, operate on, and extract information from digital images. Image processing is an increasing relevant and rapidly expanding field that underpins numerous applications in our daily life, from more traditional image enhancement methods to the most advance computer vision developments.
This course presents the fundamental principles of image processing through the extension of signal processing techniques to images. It will give students a solid foundation upon which they will be able to build solutions to image processing problems. Building on prior knowledge of basic univariate time series analysis, the course introduces basic concepts of vision, images and operators, and diverse image transforms. The course then considers how the previously presented concepts are applied to tackle some of the key challenges in image processing, including problems in image enhancement, image restoration, and image segmentation. The course then finalises considering extensions of the techniques above, including their application to multispectral images.
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| Course description |
Lectures and tutorials
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
| Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Formative Assessment Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
62 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Coursework |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply, analytically for small images and algorithmically for larger ones, key techniques for image transformation, enhancement, restoration and segmentation.
- Appraise the advantages and disadvantages of diverse techniques to solve problems and tasks in image processing.
- Generalise signal processing techniques from univariate signals to multidimensional data.
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Reading List
Essential
¿ Rafael C Gonzalez & Richard E Woods, "Digital Image Processing", 4th Edition, Pearson, 2018, ISBN-13: 9780133356724 (e-copies available in the library)
¿ Maria MP Petrou & Costas Petrou, ¿Image Processing: The fundamentals¿, 2nd Edition, Wiley, 2010, ISBN-13: 9780470745861 (e-copies available in the library)
Recommended for Matlab demos:
¿ Chris Solomon & Toby Breckon. ¿Fundamentals of digital image processing: A practical approach with examples in Matlab¿ Chichester: Wiley-Blackwell ; 2011. ISBN-13: 9780470689783 (e-copies available in the library)
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | image signal processing,feature extraction,segmentation and classification,transforms |
Contacts
| Course organiser | Dr Javier Escudero Rodriguez
Tel: (0131 6)50 5599
Email: |
Course secretary | Miss Jo Aitkenhead
Tel: (0131 6)50 5532
Email: |
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