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ENGINEERING TRIPOS PART IB - 2012/2013
PAPER 8 - SELECTED TOPICS (4)
Leaders: Prof R. Cipolla / Prof Z. Ghahramani / Prof N.G. Kingsbury
Timing: Weeks 1-4 Easter term
Structure: 14 lectures + 2 examples classes, 4 lectures/week
To teach students about image processing within the context of photo editing software (such as Adobe Photoshop or Microsoft Image Suite) and image search engines (such as Image Google). There will be quite as strong emphasis on statistical techniques (histograms) and spatial domain filtering methods which will follow on naturally from the material in paper 6 and 7.
A - Photo Editing - Lectures 1-5 (N. Kingsbury):
Part A of the course will discuss basic digital image handling techniques and will cover the following topics:
NB: All filters will be based on separable 1D Gaussian lowpass filters, with combinations of these to produce bandpass and highpass filters. These can be analysed in the spatial domain, so the 2D Fourier and Z transforms will not be taught.
B - Image Features and Matching - Lecturers 6-10 (R. Cipolla):
Part B will include material on feature and texture descriptors and efficient shift-invariant and rotation-invariant matching techniques using these descriptors. It will cover the following topics:
C - Image Searching and Modelling Using Machine Learning Methods - Lecturers 11-14 (Z. Ghahramani):
Part C of the course will focus on the application of pattern recognition and statistical machine learning methods applied to image retrieval and related problems. Although all examples will focus on applications to images, the idears are generally applicable to other domains, for example, web document retrieval, music, and financial data. We will cover the following topics:
Please see the Booklist for Part IB Courses for references for this module.
Last updated: May 2012