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 Academic Year: | 2017/8 |
| Owning Department/School: | Department of Computer Science |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| 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: | Undergraduate students selecting this unit should note the teaching structure |
| Description:
| Aims: To understand the fundamentals 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. Apply techniques in low-level vision, linear and non-linear filtering, feature extraction and description. 2. Understand visual modelling of geometry and appearance, and techniques for estimation and inference from images. 3. Assimilate current work in the field and devise an independent research area. 4. Plan and execute a research and development project and evaluate this in the context of the state-of-the-art. Skills: Linear Algebra (tfa), Probability (tfa), Geometry (tfa), Programming and Development (tfa). Content: Image Formation, Image Processing, Linear Filtering, Gradients, Blurring, Scale-Space and Pyramids. Image Features, Corners, Lines, Blobs, Harris, Canny. Descriptors, SIFT. Projection, Planar and Epipolar Geometry. Image Stitching and SFM. RANSAC, Bundle Adjustment. Dense Matching. Stereo and Optical Flow. Introduction to Object Recognition, Introduction to Learning for Vision. |
| Programme availability: |
CM50248 is Compulsory on the following programmes:Department of Computer Science
CM50248 is Optional on the following programmes:Department of Computer Science
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Notes:
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