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Motion Detection Using Spiking Neural Network Model

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wu, Qingxiang, McGinnity, TM, Maguire, LP, Cai, Jianyong and Valderrama, German (2008) Motion Detection Using Spiking Neural Network Model. In: ICIC '08 Proceedings of the 4th international conference on Intelligent Computing - with Aspects of Artificial Intelligence, Shanghai, China. Springer-Verlag Berlin, Heidelberg ©2008. 8 pp. [Conference contribution]

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URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.1725&rep=rep1&type=pdf.

DOI: 10.1007/978-3-540-85984-0_10

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 by the spiking neural network. 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.

Item Type:Conference contribution (Lecture)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:20362
Deposited By:Mr German Valderrama
Deposited On:28 Oct 2011 10:03
Last Modified:28 Oct 2011 10:03

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