The assessment for the module will be based on a combination of coursework and an open-book exam. The coursework
consists of two parts: reports on a set of practical assignments carried out using MATLAB and an essay based on
reading a collection of journal papers.
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You must have completed all the webcourse practicals (Labs) before attempting these projects. If you haven't completed these practicals you will not have the software necessary to attempt the assesed work. You should carry out and write reports on the set of practical exercises described in the links below;
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The exercises are to be performed using the MATLAB image environment ( MATLAB hints). A moderate
degree of collaboration in carrying out the work is acceptable but the reports must be entirely your own work. You can expect
to have to draw upon both course work and previous practical experience. In each case you should explain the objective of the
exercise, discuss the motivation for the approach, outline the combination of processes you used, explain the results you
obtained, discuss the scope (ie general usefulness) of the method and try to address any specific questions raised. Your report
should be illustrated with images showing stages in the processing. You do not need to explain the basics of MATLAB. A guide
to the length of each report and the number of marks available will be provided with the project description.
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You will be given six papers that describe various approaches to the problem of detecting and identifying
faces in images, and then asked to write a 1500-2000 word essay on the subject.
Instructions for this essay are given in this PDF document.
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Students wiil be asked to give a 15 minute presentation on one of the papers provided for the assessed essay task.
Details are given in this PDF document.
The papers required for both the essay and presentation tasks are available below.
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Paper
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Download
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MultiLinear Analysis of Image Ensembles: TensorFaces
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PDF (822kB)
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Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection
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PDF (536kB)
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Eigenfaces for Recognition
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PDF (10.1MB)
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Probabilistic Visual Learning for Object Representation
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PDF (4.3MBB)
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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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PDF (576kB)
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Rapid Object Detection using a Boosted Cascade of Simple Features
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PDF (194kB)
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The course will have a 2-hour examination. This open-book examination will cover all aspects of the course. Course notes, textbooks and any other relevant materials can be taken into the examination room.
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Course Aspect
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Date
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First assessed MATLAB practical
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Submitted by Wednesday 25th March 2009
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Second assessed MATLAB practical
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Submitted by Wednesday 22nd April 2009
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Assessed essay
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Submitted by Friday 15th May 2009
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The final examination
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To be announced
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