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A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling. / Wei, Hua-Liang; Zhao, Yifan; Jiang, Richard.
2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, 2015. p. 119-124.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Wei, H-L, Zhao, Y & Jiang, R 2015, A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling. in 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, pp. 119-124. https://doi.org/10.1109/ICCSE.2015.7250228

APA

Wei, H.-L., Zhao, Y., & Jiang, R. (2015). A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling. In 2015 10th International Conference on Computer Science & Education (ICCSE) (pp. 119-124). IEEE. https://doi.org/10.1109/ICCSE.2015.7250228

Vancouver

Wei HL, Zhao Y, Jiang R. A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling. In 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE. 2015. p. 119-124 doi: 10.1109/ICCSE.2015.7250228

Author

Wei, Hua-Liang ; Zhao, Yifan ; Jiang, Richard. / A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling. 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, 2015. pp. 119-124

Bibtex

@inproceedings{c967b27852fc42428f966a023ef93f6e,
title = "A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling",
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 spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.",
keywords = "Spatio-temporal systems, wavelet neural networks, system identification, learning from data",
author = "Hua-Liang Wei and Yifan Zhao and Richard Jiang",
year = "2015",
month = jul,
day = "22",
doi = "10.1109/ICCSE.2015.7250228",
language = "English",
isbn = "9781479965984",
pages = "119--124",
booktitle = "2015 10th International Conference on Computer Science & Education (ICCSE)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - A Wavelet Neural Network Model for Spatio-Temporal Image Processing and Modeling

AU - Wei, Hua-Liang

AU - Zhao, Yifan

AU - Jiang, Richard

PY - 2015/7/22

Y1 - 2015/7/22

N2 - 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 spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.

AB - 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 spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.

KW - Spatio-temporal systems

KW - wavelet neural networks

KW - system identification

KW - learning from data

U2 - 10.1109/ICCSE.2015.7250228

DO - 10.1109/ICCSE.2015.7250228

M3 - Conference contribution/Paper

SN - 9781479965984

SP - 119

EP - 124

BT - 2015 10th International Conference on Computer Science & Education (ICCSE)

PB - IEEE

ER -