Computer Vision - 2025 entry
MODULE TITLE | Computer Vision | CREDIT VALUE | 15 |
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MODULE CODE | ECMM426 | MODULE CONVENER | Dr Sareh Rowlands (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 0 | 15 | 0 |
Number of Students Taking Module (anticipated) | 75 |
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How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars)
This module expects prior knowledge in linear algebra and probability theory (e.g., ECM1416), programming with Python (ECMM1400), object-oriented programming (ECM1414 and ECM1410). Prior knowledge in machine learning is also desirable (such as ECM3420 or ECMM422).
This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
2. Design and implement vision algorithms in a high-level language.
Discipline Specific Skills and Knowledge
4. Select appropriate statistical representations, features and algorithms to suit problem specificities.
Personal and Key Transferable / Employment Skills and Knowledge
6. Critically read and report on research papers.
The course will cover the following topics:
Image formation: geometry, light, and cameras
Image processing: convolution, linear filters, Fourier transforms, image gradients, geometric transformations
Feature extraction & matching: corners, edges, blobs, and lines; feature descriptors (SIFT), feature matching and tracking
Object detection and recognition: K-NN, bag-of-words, scanning windows & Viola-Jones
Image segmentation: active contours, Markov random fields, graph cuts
Dense image correspondences: dense motion estimation, optical flow, stereo
Shape reconstruction: 2D and 3D shape modelling and fitting, active appearance models, 3D morphable models
3D vision: 3D pose estimation, calibration, structure from motion, SLAM, shape from shading, motion capture
Deep learning for vision: neural networks, convolutional neural networks, object detection, semantic and instance segmentation, recurrent neural networks
Scheduled Learning & Teaching Activities | 33 | Guided Independent Study | 117 | Placement / Study Abroad | 0 |
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Category |
Hours of study time |
Description |
Scheduled Learning & Teaching activities |
22 |
Lectures |
Scheduled Learning & Teaching activities |
11 |
Workshops/tutorials |
Guided independent study |
48 |
Coursework preparation |
Guided independent study |
69 |
Wider reading and self study |
N/A
Coursework | 70 | Written Exams | 30 | Practical Exams | 0 |
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Form of Assessment |
% of Credit |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
---|---|---|---|---|
Coursework: workshop code |
70 |
48 hours, code submission |
All |
Written feedback and model |
Quiz |
30 |
2 hours |
1, 3, 4, 5, 6 |
Written feedback via ELE |
Original Form of Assessment |
Form of Re-assessment |
ILOs Re-assessed |
Time Scale for Re-assessment |
---|---|---|---|
Coursework: workshop code |
Coursework: workshop code (70%) |
All |
Referral/deferral period |
Quiz |
Quiz (2 hours, 30%) |
1, 3, 4, 5, 6 |
Referral/deferral period |
Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
ELE: 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/
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 | Szeliski, Richard | Computer vision: algorithms and applications | 2nd | Springer | 2021 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Tuesday 10th July 2018 | LAST REVISION DATE | Thursday 24th April 2025 |
KEY WORDS SEARCH | Computer vision, object recognition and detection, semantic and instance segmentation, tracking, pattern recognition, deep learning. |
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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.