Machine Vision - 2024 entry
MODULE TITLE | Machine Vision | CREDIT VALUE | 15 |
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MODULE CODE | COMM413DA | MODULE CONVENER | Unknown |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS |
Number of Students Taking Module (anticipated) | 90 |
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- Image processing: convolution, linear filters, image gradients
- Feature extraction & matching: edge & corner detection, multi-scale analysis, feature descriptors, feature matching and tracking
- Geometric Image formation: geometric transformations, pinhole camera and perspective effects
- Multi-view geometry and structure from motion: 3D reconstruction
- Recognition in Computer Vision: classical approaches for image classification, and object detection, k-nearest neighbours and support vector machine classifiers. Deep learning for computer vision: neural networks, convolutional neural networks, object detection, semantic and instance segmentation.
Scheduled Learning & Teaching Activities | 40 | Guided Independent Study | 110 | Placement / Study Abroad | 0 |
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Category | Hours of study time | Description |
Scheduled Learning and Teaching | 40 | Lectures, Practicals |
Guided independent study | 110 | Reading, preparation, coursework |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Practical work | 16 hours | All | Oral |
Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Programming practice and written essay | 40 | 8 hours, code+report | All | Written feedback and model answers |
Programming practice and written essay | 60 | 12 hours, code + report | All | Written feedback and model answers |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Summative assessment |
Programming practice and written essay (100%)
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All | August re-assessment period |
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
ELE: http://vle.exeter.ac.uk/
Web based and Electronic Resources:
CVOnline: an online compendium of computer vision techniques: http://homepages.inf.ed.ac.uk/rbf/CVonline/
Website of R. Szelinski’s Computer Vision book (including a free electronic version of the book): http://szeliski.org/Book
Getting Started with MATLAB: http://uk.mathworks.com/help/matlab/getting-started-with-matlab.html
Computer Vision System Toolbox of MATLAB: https://uk.mathworks.com/products/computer-vision.html
Keras: The Python Deep Learning library: https://keras.io/
Other Resources:
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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Set | Bishop, John C. | Pattern recognition and machine learning | Springer | 2006 | ||
Set | Forsyth, David & Jean Ponce | Computer vision: a modern approach | 2nd | Pearson | 2011 | |
Set | Jan Erik Solem | Programming Computer Vision with Python: Tools and algorithms for analyzing images | O'Reiley | 2012 | ||
Set | Szeliski, Richard | Computer vision: algorithms and applications | 2nd | Springer | 2021 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | COMM416DA |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Thursday 6th July 2017 | LAST REVISION DATE | Thursday 1st August 2024 |
KEY WORDS SEARCH | Computer vision, image processing, object recognition, object detection and segmentation, tracking, pattern recognition. |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.