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 Academic Year: | 2017/8 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 12 [equivalent to 24 CATS credits] |
| Notional Study Hours: | 240 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
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| Assessment Summary: | CW 100% |
| Assessment Detail: |
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| Supplementary Assessment: |
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| Requisites: |
Before or while taking this module you must take CM50248
Undergraduate students selecting this unit should note the teaching structure |
| Description:
| Aims: To understand and critically evaluate the fundamental and advanced aspects of computer vision and be equipped with the skills needed to specify and undertake an independent project. Learning Outcomes: Upon Completion of this unit students will be able to: 1. Describe state-of-art techniques in mid-level and high-level computer vision. 2. Understand the relationship between the segmentation, classification and identification of images and video. 3. Explain the importance of Machine Leaning to Computer Vision. 4. Demonstrate the practical application of theory to experiment. Skills: Linear Algebra (TFA), Probability (TFA), Programming (TFA), Experimentation (TFA) Content: Segmentation by clustering: GMMs, mean-shift, graph-cuts, and Berkeley's segmentation algorithm. Object Recognition: bag-of words, model fitting with AAMs, and DPMs. Experimental assessment methods: PR curves, confusion matrices. Applications of Computer Vision. |
| Programme availability: |
CM50249 is Optional on the following programmes:Department of Computer Science
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Notes:
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