Spiking neural network performs discrete cosine transform for visual images

Qingxiang Wu, T. M. McGinnity, Liam Maguire, Arfan Ghani, Joan Condell

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    7 Citations (Scopus)

    Abstract

    The human visual system demonstrates powerful image processing functionalities. Inspired by the principles from neuroscience, a spiking neural network is proposed to perform the discrete cosine transform for visual images. The structure and the properties of the network are detailed in this paper. Simulation results show that the network is able to perform the discrete cosine transform for visual images. Based on this mechanism, the key features can be extracted in ON/OFF neuron arrays. These key features can be used to reconstruct the visual images. The network can be used to explain how the spiking neuron-based system can perform key feature extraction. The differences between the discrete cosine transform and the spiking neural network transform are discussed.
    Original languageEnglish
    Title of host publicationEmerging Intelligent Computing Technology and Applications
    Subtitle of host publicationWith Aspects of Artificial Intelligence
    PublisherSpringer Verlag
    Pages21-29
    Number of pages9
    ISBN (Print)97836420402073
    DOIs
    Publication statusPublished - 2009

    Keywords

    • spiking neural networks
    • visual system
    • discrete cosine transform
    • visual image

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