CM30080: Computer vision
[Page last updated: 04 August 2021]
| Academic Year: | 2021/2 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Honours (FHEQ level 6) |
| Period: |
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| Assessment Summary: | CW 50%, EX 50% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: | Before taking this module you must take CM20219 AND take CM20220 |
| Aims: | To understand the ways of analysing images to get information out of them. In order to achieve this it will be necessary to understand the underlyin mathematics and computer techniques. |
| Learning Outcomes: | Students will be able to:
1. Distinguish low-level from high-level Computer Vision methods, and appreciate the vision problem; 2. Describe edge detection as a linear filter and distinguish between linear filtering and morphology; 3. Describe multi-camera geometry and understand its value in applications such as mosaicing and reconstruction; 4. Understand texture and segmentation, and the role of high-level models in recognition; 5. Appreciate a broad range of contemporary Computer Vision. |
| Skills: | Application of number (T/F). |
| Content: | Low level vision: Convolution and linear filtering, edge detection and blurring; the role of scale. Morphology. Texture descriptors.
Multi-camera vision: Homographies, epipolar geometry, and the fundamental matrix. Mosaicing and 3D reconstruction.
Segmentation: Hough transforms, unsupervised clustering, scale sieves.
Recognition: The role of prior models: templates, geometry, and statistics. |
| Programme availability: |
CM30080 is Optional on the following programmes:Department of Computer Science
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Notes:
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