CM32022: Advanced computer vision
[Page last updated: 22 April 2025]
| Academic Year: | 2025/26 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 10 [equivalent to 20 CATS credits] |
| Notional Study Hours: | 200 |
| Level: | Honours (FHEQ level 6) |
| Period: |
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| Assessment Summary: | CWSG 50%, EXCB 50% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: |
In taking this module you cannot take CM52035
Before taking this module you must ( take CM22009 AND take CM22010 ) OR ( take CM20315 AND take CM20219 ) |
| Learning Outcomes: |
1. Describe and implement advanced image processing algorithms;
2. Describe the mathematics of multi-view geometry and their applications to 3D reconstruction;
3. Understand classical and learning-based low- and high-level computer vision techniques;
4. Appreciate a broad range of contemporary computer vision and its applications. |
| Synopsis: | Building on Visual Computing (year 2) and Machine Learning (year 2), you will explore advanced ways of processing images to extract information and models, and gain a deeper understanding of the underlying mathematics. Among other topics, you will learn about image processing, recognition and 3D reconstruction. You will also learn about advanced deep learning techniques, as well as their applications. |
| Content: | Image processing: feature detection; scale spaces and pyramids; compressed sensing
Multi-camera vision: planar geometry; epipolar geometry; the fundamental matrix
3D reconstruction; depth estimation; triangulation; structure from motion
Advanced classical and deep learning techniques for vision problems such as segmentation, detection, recognition; classification and regression models
Contemporary topics and applications of computer vision |
| Course availability: |
CM32022 is Optional on the following courses:Department of Computer Science
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Notes:
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