ENGINEERING TRIPOS PART IB - 2012/2013

PAPER 8 - SELECTED TOPICS (4)

Information Engineering Option

Photo Editing and Image Searching

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

AIMS

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.

Lecture Syllabus

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:

• Cropping, resizing, rotation and morphing - involving basic ideas of interpolation and filtering for shifting/resampling purposes.
• Colour - conversion between different colour spaces (e.g. RGB, YUV and HSV) and adjustment for colour lighting effects such as colour-cast correction and white balancing.
• Histograms - their use in analysis and correction of lighting intensity problems, such as over/under exposure and shadows.
• Segmentation - for purposes such as red-eye correction and independent contrast correction in areas of shadows, mid-tones and highlights.
• Correcting focus problems - sharpening (debluring) filters and problems of noise amplification.
• Correcting noise problems - smoothing filters, problems of blurring, and the use of spatially adaptive filters to optimise sharpening and denoising tradeoffs.
• These will be illustrated with the development of Matlab solutions to a range of common photo editing functions such as found in widely used packages like Adobe Photoshop and Microsoft Digital Image Suite.

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:

• Convolution with gaussians and derivatives of gaussians to provide directional bandpass filters.
• Edge detection using directional filters.
• Interest point detection using edge measurement and image autocorrelation measurement.
• Texture descriptors, based on filters or on principle components analysis (PCA) of images
• The SIFT feature descriptor for matching image features
• A case study of a real-time industrial system to match a photograph from a mobile phone to images in a database, and applications of such systems

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:

• Representing images as feature vectors
• Probabilistic models, use of Bayes rule, Bernoulli distributions and multivariate Gaussians
• Image retrieval using nearest neighbours
• Outlier removal and novelty detection
• A case study of a state-of-the-art image retrieval method

Please see the Booklist for Part IB Courses for references for this module.

Last updated: May 2012