Here are my honours projects for 2005.
IMPORTANT: Please come and see me regarding any project you intend doing before selecting that project.

Kit: Organic Modelling with Generalised Cylinders
  Interactive Adaptive Learning Systems
  The Illusion of Beauty
See also: Honours Projects 2004
Honours Projects 2003


Organic Modelling with Generalised Cylinders (12/20pt project) JM1

Generalised cylinders are a geometric modelling method, originally developed for use in computer vision. For this project we wish to apply them to the modelling of organic structures for computer graphics. The basic principle for creating a generalised cylinder is to define a series of cross-sectional profiles, possibly of varying shape and size, and distribute them over some continuous curve, known as the carrier curve. The cross-sections are connected to form a continuous surface. Sounds easy, but there are a number of important issues that need to be addressed to ensure that the geometry defined by the cylinder is legal (i.e. can be rendered). Constructing compound surfaces is very useful for modelling organic structures such as branches, leaves, tentacles, veins, shells, etc., as this image (below) illustrates. The image is procedurally generated using generalised cylinders.

The challenge for this project will be to create a software system to assist in the automated construction of such models using generalised cylinders. The system will also have to deal with managing geometric complexity and geometric output in a variety of formats (e.g. real-time, offline rendering). The software for the project should be written in C++ and OpenGL. You should have successfully completed CSE3313 Computer Graphics (or equivalent) in order to work on this project.

Preliminary Reading:
McCormack, J. 2004, Generative Modelling with Timed L-Systems, in Gero, J.S. (ed) Design Computing and Cognition '04, Kluwer Academic Publishers, Dordrecht. pp. 157-175. (Hargrave Library reference: H 620.00420285 I61.4D 2004)

Prusinkiewicz, P., L. Mündermann, R. Karwowski & B. Lane 2001, The Use of Positional Information in the Modeling of Plants. Proceedings of SIGGRAPH 2001 (Los Angeles, California, August 12-17). In Computer Graphics (Proceedings) Annual Conference Series, ACM SIGGRAPH, pp. 289-300.

Mech, R., P. Prusinkiewicz & J. Hanan 1997, 'Extensions to the Graphical Interpretation of L-Systems Based on Turtle Geometry', Technical Report, No. 1997-599-01, April 1, 1997. University of Calgary, Calgary, Alberta Canada.

Interactive Adaptive Learning Systems (12/20pt project) JM2

This project will investigate the use of adaptive learning systems for a real-time interactive application. Collections of virtual creatures are required to develop a symbiotic relationship with a human audience – responding to movement and gesture. Students should be prepared to investigate a number of adaptive learning techniques including classifier systems and evolutionary ANN (Artificial Neural Network) approaches, with the goal of creating a novel system that evolves and adapts to its real and virtual environments. The key emphasis for this system must be flexibility and real-time behaviour as the dynamic environment is actually changing in real-time.

General Introductions to Adaptive and Evolutionary Systems (all available from the Hargrave library):

Flake, G.W. (1998), The Computational Beauty of Nature : Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation, MIT Press, Cambridge, Mass.

Pfeifer, R. and C. Scheier (1999), Understanding Intelligence, MIT Press, Cambridge, Mass.

Holland, J.H. (1995), Hidden Order : How Adaptation Builds Complexity, Helix Books, Addison-Wesley, Reading, Mass.

More specific papers:

McCormack, J. (2002), Evolving for the Audience, International Journal of Design Computing 4 (Special Issue on Designing Virtual Worlds). pdf version.

McCormack, J. (2003), Evolving Sonic Ecosystems, Kybernetes 32(1/2). pdf version.

The Illusion of Beauty (20pt project) JM3

What is beauty and what makes a thing beautiful? Is beauty a property of things and can it be measured? This was famously answered by the mathematician Birkhoff who in 1933 proposed an 'aesthetic measure', equal to the ratio of order to complexity. Birkhoff was able to measure the aesthetics of simple shapes and vases. However, the measure was not successful in general. Many principles of what makes a good picture are well known, i.e. in composition we talk of balance, entrance and exit, symmetry, etc. as properties of what is considered a good composition. For this project the challenge is to try and encode or infer rules about visual composition to see if we can devise a better aesthetic measure of an image. This could have all sorts of applications, for example, automated selection of camera positions in 3D virtual environments, use as "fitness functions" to be used in evolutionary programs and so on — read this paper as a good starting point. Would we think a computer that can make beautiful images as being creative?

Preliminary Reading:

Saunders, R and Gero, JS: 2001, Artificial Creativity: A Synthetic Approach to the Study of Creative Behaviour, in Gero, JS (ed), Proceedings of the Fifth Conference on Computational and Cognitive Models of Creative Design, Key Centre of Design Computing and Cognition, Sydney.

Humphrey, NK: 1973, The Illusion of Beauty, Perception 2: 429-439.

Solomonoff, RJ: 1995, The discovery of algorithmic probability: A guide for the programming of true creativity., in Vatanyi, P (ed), Computational Learning Theory: EuroCOLT '95, Springer-Verlag, Berlin, pp. 1-22.

Boden, MA: 1994, What is Creativity?, in Boden, MA (ed), Dimensions of Creativity, MIT Press, Cambridge, MA, pp. 75-117.

Dartnall, T (ed.) 2002, Creativity, Cognition, and Knowledge: An Interaction, Praeger, Westport, Connecticut; London.

Partridge, D and Rowe, J: 1994, Computers and creativity, Intellect, Oxford, England.