RCW/1
Project
1: Handwritten Postcode Recognition [CS,CS/M]
Aims: The aims of this project are
- Segment the characters in handwritten postcodes
- Recognise the characters and reconstruct the postcode
- Evaluate the accuracy of the method
Background: Many millions of letters are
sent through the post each day. Business letters almost always have
printed addresses and are relatively easy to read automatically using
OCR (Optical Character Recognition). Handwritten letters however
present a more challenging problem because of the huge variation in
handwritten. This project will focus on a subproblem; reading
handwritten postcodes.
Handwriting recognition is generally split into two stages;
segmentation, where the writing is split into individual character
images, and recognition, where the character images are converted into
characters. In this project you will be expected to implement
segmentation and recognition on postcodes.
Reading
Some concepts of character recognition, A. W. Holt,
1976
Matteson, R (1995). Introduction
to Document Image Processing Techniques. Artech House.
Trier, Ø. Jain, A. Taxt, T. (1995). Feature Extraction Methods for
Character Recognition – A Survey. Pattern
Recognition. Vol 29. No. 4. pp 641-662
RCW/2
Project
2: Structure from motion [CS,CS/M]
Aims: The aim of this project is to reconstruct the
shape of an object from an sequence of images where the object (or
camera) is moving.
Background and method: It is well known that the
shape of an object can be found using a pair of stereo images of the
same object, but from different locations. It is also possible to find
the shape of an object from images taken at different times, provided
that either the object or the camera is moving. Structure from motion
solves this problem. The solution is divided into three parts; 1)
finding the optical flow between images, 2) calibrating the camera and
motion, and 3) finding the shape. In this project, you will implement
methods to tackle these problems and extract the shape of an object.
Reading
Richard
Hartley and Andrew Zisserman (2003). Multiple View Geometry in
Computer Vision. Cambridge University Press.
T. Huang and A. Netravali.
Motion and structure from feature correspondences: A review.
Proceedings of the IEEE, 82(2), 1994.
RCW/3
Project
3: Graph descriptors [CS,MEng,CS/M,MMath]
Aims: The aim of this project are to design and test
the effectiveness of graph descriptors for uniquely distinguishing
between small graphs. You will need to do the following
- Design and implement a set of graph descriptors
- Generate graph sets for testing
- Compare the effectiveness of the descriptors for
distinguishing between graphs
Background and method: Graphs are difficult to
compare to each other, since there is no known polynomial-time
algorithm for deciding if two graphs are the same (isomorphic). A graph
descriptor is a number (or set of numbers) which can be used to
represent a graph and can be computed relatively easily. An example is
the number of edges in the graph. The idea is that, if two graphs are
the same, then the descriptor should be the same, and if two graphs are
different, then the descriptor should be different. There is no known
descriptor which satisfies this property, as it would solve the
isomorphism problem above, but some are better than others in the sense
that they often uniquely describe the graph, but not always. In this
project you will design and test descriptors to see how good they are
on small graphs. M-level students will be expected to design novel
descriptors and test their effectiveness.
Reading
P. Zhu
and R. C. Wilson.
Stability of the eigenvalues of graphs.
Computer Analysis Of
Images And Patterns, Proceedings, 3691:371-378, 2005.
R. C. Wilson, E. R. Hancock, and B. Luo.
Pattern vectors from algebraic graph theory.
IEEE Transactions On Pattern Analysis And Machine
Intelligence, 27(7):1112-1124, July 2005
Willem H. Haemers,
Edward Spence:
Enumeration of cospectral
graphs.
Eur. J. Comb. 25(2): 199-211 (2004)
RCW/4
Project
4: Identifying Galaxies [CS,MEng,CS/M,MMath]
Background and Method: Modern telescopes return an
enormous
about of image data. Some of these are 'deep fields' which contain
images of many thousands of galaxies. One of the things that
astronomers would like to do is classify and count these galaxies into
different types, as this gives some important insights into the history
of the Universe. In fact there is an online project in which people can
participate by identifying galaxies by eye (www.galaxyzoo.org). The aim
of this project is to design and implement a method of automatically
classifying them. You will need to investigate and design image
features which will distinguish the different types and build a
classifier to sort them into the different types. At M-level you will
need to create new descriptors and compare their performance against
some of the alternatives in the literature.
Reading
www.galaxyzoo.org
http://www.sdss.org/
Identification and
classification of galaxies using a biologically-inspired neutral network,
Radhakhrishna Somanah,
Soonil D.D.V. Rughooputh and
Harry C.S. Rughooputh, Astrophysics and Space
Science, 282(1)
pp161-169, 2002
RCW/5
Project
5: Musical Genre Classification [CS,CS/M,MEng,MMath,MScIT]
Aims: The aim of this project is to design features
and a
classifier which will sort music into a number of different genres. The
main tasks are
- Investigating and extracting features from music which may
separate different genres.
- Implementing a classifier based on these features.
- Evaluating the success of the classification.
Background and Method:
Sorting
music into different genres is an extremely useful task when organising
a large database of songs. While the classification of music is
somewhat subjective, there are features which can distinguish different
styles. For example, instrumental music can be distinguished from songs
by looking for stable frequencies in the spectrogram. In this project
you will look for similar features in the time and frequency domain
which can distinguish more different musical genres or, for an M-level
student, you will need to devise a new method of analysis which can
separate the different genres.
http://www.hpl.hp.com/techreports/2003/HPL-2003-183.pdf
Automatic Music
Classification and Summarization, C Xu, N C Maddage and X.
Shao, IEEE Transactions on Speech and Audio Processing, 13(3) pp441-450
2005
RCW/6
Project
6:
Shot Segmentation in Videos [CS,MEng,CS/M,MMath]
In order to produce useful video libraries, it is often
important to split a
video sequence up into it's shots or scenes. Each shot is taken from
one camera,
and each scene is set in one specific location. The aim of this project
is to
devise a method for segmenting a video clip using either the geometry
of the
image for shots, or features of the location for scenes. There are a
number of
features of video frames which can be used to distinguish shots,
ranging from
colour histograms and texture analysis to speed of motion. In this
project, the
student will either implement such a method or, for an M-level project,
devise a new method and
apply it
to segmenting shots in short video clips.
Reading
http://www.nada.kth.se/cvap/VIBES/
Handbook of image and video processing,
Al Bovik(ed), San Diego : Academic Press, 2000
Computer Vision, Ballard and Brown,
Prentice Hall
Computer and Robot Vision, Haralick and
Shapiro, Addison Wesley