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CM50208: Computer vision

Follow this link for further information on academic years Academic Year: 2013/4
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period: Semester 2
Further information on unit assessment Assessment: CW 25%, EX 75%
Further information on supplementary assessment Supplementary Assessment: Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Further information on descriptions Description: Aims:
To give students an advanced level understanding of current research issues in computer vision. To focus upon computer vision methods, computer vision theory, evaluation of methods and modeling vision problems. To study the latest applications of computer vision in traditional areas of science, engineering, etc, and also in new applications for entertainment, education, smart homes, etc.

Learning Outcomes:
Students will be able to:
* Obtain an in-depth understanding of computer vision theory and methods in state-of-the-art computer vision research;
* Appreciate a broad range of contemporary Computer Vision;
* Develop a systematic understanding of vision software development;
* Understand the interdisciplinary nature of advanced computer vision and to be able to combine theories and principles to achieve correctly engineered simulations;
* Distinguish low-level from high-level Computer Vision methods;
* Describe edge detection as a linear filter and distinguish between linear filtering and morphology;
* Describe multi-camera geometry and understand it value in applications such as mosaicing and reconstruction;
* Understand texture and segmentation, and the role of high-level models in recognition.

Skills:
Ability to apply modern methods and algorithms to vision solutions (T,F,A), wide knowledge of computer vision methods (T/F/A), communication skills (F/A).

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.
Further information on programme availabilityProgramme availability:

CM50208 is Optional on the following programmes:

Department of Computer Science
  • RSCM-AFD02 : Doctor of Engineering (EngD) in Digital Media (Full-time)

Notes:
* This unit catalogue is applicable for the 2013/4 academic year only. Students continuing their studies into 2014/15 and beyond should not assume that this unit will be available in future years in the format displayed here for 2013/14.
* Programmes and units are subject to change at any time, in accordance with normal University procedures.
* Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.