NDLI: Application of particle swarm system as a novel parameter optimization technique on spatiotemporal retina model

Content Provider	IEEE Xplore Digital Library
Author	Niu, X. Qiu, Y. Tong, S. Zhu, Y.
Copyright Year	2007
Description	Author affiliation: Shanghai Jiao Tong Univ., Shanghai (Niu, X.; Qiu, Y.; Tong, S.; Zhu, Y.)
Abstract	Center-surround spatiotemporal (ST) filter is a powerful tool to simulate the spatial and temporal properties of retina ganglion cells and encode visual information with electric spikes. This paper introduces the application of particle swarm optimization (PSO) algorithm to tune the parameters in the retina model consisting of a ST filter module and a back-propagation (BP) neural network module. Images are converted into electric spikes by the ST filters whose outputs are then fed into the BP neural network to reconstruct the output images. The parameters of the ST filters determine the electric spike sequences as well as the output image from the BP network. In order to get the expected output images, we employ PSO to iteratively tune the parameters. Euclidean distance between output and input image is used as scalar criteria to optimize the ST filter. The tuning process stops until the similarity between output and input images no longer improves. The results show that 62.3 % of the images trained by PSO have better output image quality and less iteration time compared with those trained by the current evolution strategy (ES).
Starting Page	5794
Ending Page	5797
File Size	384716
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4353664
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Spatiotemporal phenomena Retina Neural networks Power system modeling Information filtering Information filters Image converters Image reconstruction Euclidean distance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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