Artificial Visual Percepts for Image Understanding
Commenced in January 2007
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Edition: International
Paper Count: 32799
Artificial Visual Percepts for Image Understanding

Authors: Jeewanee Bamunusinghe, Damminda Alahakoon


Visual inputs are one of the key sources from which humans perceive the environment and 'understand' what is happening. Artificial systems perceive the visual inputs as digital images. The images need to be processed and analysed. Within the human brain, processing of visual inputs and subsequent development of perception is one of its major functionalities. In this paper we present part of our research project, which aims at the development of an artificial model for visual perception (or 'understanding') based on the human perceptive and cognitive systems. We propose a new model for perception from visual inputs and a way of understaning or interpreting images using the model. We demonstrate the implementation and use of the model with a real image data set.

Keywords: Image understanding, percept, visual perception.

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[1] S. M. Potter, What can Artificial Intelligence get from Neuroscience? Springer-Verlag, 2007, pp. 174-185.
[2] K. M. Galotti, Cognitive psychology : in and out of the laboratory , Thomson/Wadsworth, 2008.
[3] P. O. Haikonen, The Cognitive Approach to Conscious Machines, Imprint Academic, 2003.
[4] M.B. Howes, The psychology of human cognition, Pergamon Press, 1990.
[5] B. Maund, Perception, Central problems of philosophy, Acumen Publishing Ltd, Chesham,
[Eng.], 2003.
[6] E. R. Kandel, J. H. Schwartz, and T.M. Jessell, Principles of neural science, McGraw Hill, 2000.
[7] M.F. Bear, B.W. Connors, and M.A. Paradiso, Neuroscience: exploring the brain, Philadelphia : Lippincott Williams and Wilkins, 2007.
[8] T. Kohonen, Self-organizing maps, Berlin, New York: Springer, 2001.
[9] A.K. Jain, M.N. Murty and P.J. Flynn, ÔÇÿData Clustering-, ACM Computing Surveys, 31(3) , 264-323,1999
[10] D. Alahakoon, S.K. Halgamuge, and B. Sirinivasan, ÔÇÿDynamic Self- Organizing Maps with Controlled Growth for Knowledge Discovery-, IEEE Transactions on Neural Networks, 11(3), 2000, pp. 601-614.
[11] University of Washington, Content-based image retrieval database. Website,