A wavelet neural network model for spatio-temporal image processing and modeling

Hua-Liang Wei, Zhao Yifan, Richard Jiang

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Spatio-temporal images are a class of complex dynamical systems that evolve over both space and time. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite challenging. Starting with an assumption that there is no a priori information about the true model but only observed data are available, this work introduces a new type of wavelet network that utilizes the easy tractability and exploits the good properties of multiscale wavelet decompositions to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatiotemporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.
Original languageEnglish
Publication statusPublished - 22 Jul 2015
EventThe 10th International Conference on Computer Science & Education - Cambridge
Duration: 22 Jul 2015 → …

Conference

ConferenceThe 10th International Conference on Computer Science & Education
Period22/07/15 → …

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