This thesis addresses the problem of size invariant shape recognition based on scale transformation within modulated competition neural layer. In this thesis I will present the advantages of applying neural networks in pattern recognition and study how the traditional automatic target recognition fails to recognise known patterns due to size change, cluttered backgrounds and distortion. Within the thesis we will also discuss possible ways to overcome size variance and how the combining of Selective Attention Adaptive Resonance Theory makes the system capable of recognising images with size changes, distortion and in complex backgrounds. The model is constructed based on neurophysiology experiments in vision systems. The Neural Circuit Simulation studies undertaken demonstrate the effectiveness of the proposed model in recognising 2D objects in many non-ideal visual conditions. Despite size differences from the stored memory image, difficult visual environments, including severe distortion, the simulation results indicate the model can recognise the shape stored in memory from the simulated shapes. / From the research presented in this thesis, it is concluded that the use of attentional mechanisms can enhance artificial vision systems to cope with difficult visual conditions. It is shown that feed-forward-feedback interactions with synaptic modulation are a versatile and powerful mechanism for performing many useful functions such as gain control, filtering and selective processing in neural network based vision systems. / Thesis (MEng(ComputerSystemsEng))--University of South Australia, 2004.

Thesis (Ph.D.) -- McMaster University, 1998. / Includes bibliographical references (leaves 129-144). Also available via World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (p. 49-52).

Thesis (Ph.D) -- McMaster University, 1997. / Includes bibliographical references (leaves 113-124). Also available via World Wide Web.

Thesis (M. Phil.08)--University of Hong Kong, 2008. / Also available in print.

Thesis (Ph. D.)--State University of New York at Binghamton, Department of Computer Science, 2006. / Includes bibliographical references (leaves 199-204).

Thesis (M.S.) in Electrical Engineering--University of Maine, 2007. / Includes vita. Includes bibliographical references (leaves 45-46).

Thesis (Ph.D.)--University of Tasmania, 1996.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 139-144). Also available via the Internet.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 123-129).