Abstract
Language | English |
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Title of host publication | Unknown Host Publication |
Place of Publication | Berlin Heidelberg |
Pages | 26-31 |
Number of pages | 6 |
Volume | 6840 |
Publication status | Published - 4 Oct 2011 |
Event | International Conference on Interlligent Computing (ICIC 2011) - Duration: 4 Oct 2011 → … |
Conference
Conference | International Conference on Interlligent Computing (ICIC 2011) |
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Period | 4/10/11 → … |
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Keywords
- Visual attention
- spiking neural network
- receptive field
- visual system.
Cite this
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Simulation of Visual Attention using Hierarchical Spiking Neural Networks. / Wu, Qingxiang; McGinnity, TM; Maguire, LP; Cai, Rongtai; Chen, Meigui.
Unknown Host Publication. Vol. 6840 Berlin Heidelberg, 2011. p. 26-31.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Simulation of Visual Attention using Hierarchical Spiking Neural Networks
AU - Wu, Qingxiang
AU - McGinnity, TM
AU - Maguire, LP
AU - Cai, Rongtai
AU - Chen, Meigui
PY - 2011/10/4
Y1 - 2011/10/4
N2 - Based on the information processing functionalities of spiking neurons, a hierarchical spiking neural network model is proposed to simulate visual attention. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the network are detailed in this paper. Simulation results show that the network is able to perform visual attention to extract objects based on specific image features. Using extraction of horizontal and vertical lines, a demonstration shows how the network can detect a house in a visual image. Using this visual attention principle, many other objects can be extracted by analogy.
AB - Based on the information processing functionalities of spiking neurons, a hierarchical spiking neural network model is proposed to simulate visual attention. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the network are detailed in this paper. Simulation results show that the network is able to perform visual attention to extract objects based on specific image features. Using extraction of horizontal and vertical lines, a demonstration shows how the network can detect a house in a visual image. Using this visual attention principle, many other objects can be extracted by analogy.
KW - Visual attention
KW - spiking neural network
KW - receptive field
KW - visual system.
M3 - Conference contribution
SN - 978-3-642-24552-7
VL - 6840
SP - 26
EP - 31
BT - Unknown Host Publication
CY - Berlin Heidelberg
ER -