Abstract
Inspired by the behaviour of the human visual system, a spiking neural network is proposed to detect moving objects in a visual image sequence. The structure and the properties of the network are detailed in this paper. Simulation results show that the network is able to perform motion detection for dynamic visual image sequence. Boundaries of moving objects are extracted from an active neuron group. Using the boundary, a moving object filter is created to take the moving objects from the grey image. The moving object images can be used to recognise moving objects. The moving tracks can be recorded for further analysis of behaviours of moving objects. It is promising to apply this approach to video processing domain and robotic visual systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Masland, R.H.: The Fundamental Plan of the Retina. Nature Neurosci. 4, 877–886 (2001)
Wassle, H.: Parallel Processing in the Mammalian Retina. Nature Rev. Neurosci. 5, 747–757 (2004)
Nelson, R., Kolb, H.: On and off Pathways in the Vertebrate Retina and Visual System. In: Chalupa, L.M., Werner, J.S. (eds.) The Visual Neurosciences, pp. 260–278. MIT Press, Cambridge (2003)
Demb, J.B.: Cellular Mechanisms for Direction Selectivity in the Retina. Neuron 55, 179–186 (2007)
Taylor, W.R., Vaney, D.I.: New Directions in Retinal Research. Trends Neurosci. 26, 379–385 (2003)
Kim, I.J., Zhang, Y., Yamagata, M., Meister, M., Sanes, J.R.: Molecular Identification of a Retinal Cell Type that Responds to Upward Motion. Nature 452, 478–482 (2008)
Reppas, J.B., Niyogi, S., Dale, A.M., Sereno, M.I., Tootell, B.H.: Representation of Motion Boundaries in Retinotopic Human Visual Cortical Areas. Nature 388(6638), 175–186 (1997)
Olveczky, B.P., Baccus, S.A., Meister, M.: Segregation of Object and Background Motion in the Retina. Nature 423(6938), 401–408 (2003)
Angelaki, D.E., Shaikh, A.G., Green, A.M., Dickman, J.D.: Neurons Compute Internal Models of the Physical Laws of Motion. Nature 430(6999), 560–565 (2004)
Lin, J.W., Faber, D.S.: Modulation of Synaptic Delay during Synaptic Plasticity. Trends Neurosci. 25(9), 44–55 (2002)
Pena, J.L., Kazuo, S., VSaberi, F.K., Konishi, M.: Cochlear and Neural Delays for Coincidence Detection in Owls. The Journal of Neuroscience 21(23), 9455–9459 (2001)
Senn, W., Schneider, M., Ruf, B.: Activity-Dependent Development of Axonal and Dendritic Delays, or, Why Synaptic Transmission Should Be Unreliable. Neural Computation 14, 583–619 (2002)
Carr, C.E., Konishi, M.: Axonal Delay Lines for Time Measurement in the Owl’s Brainstem. Proceedings of the National Academy of Sciences of the United States of America 85(21), 8311–8315 (1988)
Crook, S.M., Ermentrout, G.B., Vanier, M.C., Bower, J.M.: The Role of Axonal Delay in the Synchronization of Networks of Coupled Cortical Oscillators. Journal of Computational Neuroscience 4(2), 157–6873 (1997)
Kandel, E.R., Shwartz, J.H.: Principles of Neural Science. Edward Amold (Publishers) Ltd. (1981)
Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, Oxford (1999)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, Cambridge (2001)
Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, pulations, Plasticity. Cambridge University Press, Cambridge (2002)
Müller, E.: Simulation of High-Conductance States in Cortical Neural Networks, Masters Thesis, University of Heidelberg, HD-KIP-03-22 (2003)
Wu, Q.X., McGinnity, T.M., Maguire, L.P., Glackin, B., Belatreche, A.: Learning Mechanism in Networks of Spiking Neurons. Studies in Computational Intelligence, vol. 35, pp. 171–197. Springer, Heidelberg (2006)
Wu, Q.X., McGinnity, T.M., Maguire, L.P., Belatreche, A., Glackin, B.: Adaptive Co-Ordinate Transformation Based on Spike Timing-Dependent Plasticity Learning Paradigm. In: Wang, L., Chen, K.S., Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 420–428. Springer, Heidelberg (2005)
Jeffress, L.A.: A Place Theory of Sound Localization. J. Comp. Physiol. Psychol. 41, 35–39 (1948)
Jeffress, L.A.: Binaural Phase Difference and Pitch Variation. Am. J. Psychol. 61, 468–486 (1948)
Jeffress, L.A.: Interaural Phase Difference and Pitch Variation: Day-to-Day Changes. Am. J. Psychol. 62, 1–19 (1949)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Q., McGinnity, T.M., Maguire, L., Cai, J., Valderrama-Gonzalez, G.D. (2008). Motion Detection Using Spiking Neural Network Model. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)