Artificial Visual Percepts for Image Understanding
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.
[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,
http://www.cs.washington.edu/research/imagedatabase/groundtruth/.
[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,
http://www.cs.washington.edu/research/imagedatabase/groundtruth/.
@article{"International Journal of Information, Control and Computer Sciences:62331", author = "Jeewanee Bamunusinghe and Damminda Alahakoon", title = "Artificial Visual Percepts for Image Understanding", abstract = "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.", volume = "4", number = "5", pages = "1018-8", }