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Synoptic Maps Forecast Using Spatio-temporal Models

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4739))

Abstract

The objective of this paper is to study several approaches to forecasting the temporal evolution of meteorological synoptic maps that carry information in visual form but without objects. Window-based descriptors are used in order to accomplish continuity so the prediction task is possible. Linear and non-linear models are applied for the prediction task, the first one being based on a spatio-temporal autoregressive (STAR) model whereas the second one is based on artificial neural networks. The method and obtained results are discussed.

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References

  1. Bors, A., Pitas, I.: Prediction and tracking of moving objects in image sequences. IEEE Trans. Image Processing 8, 1441–1445 (2000)

    Article  Google Scholar 

  2. Crespo, J.L., Bernardos, P., Zorrilla, M.E., Mora, E.: Preprocessing Phase in the PIETSI Project (Prediction of Time Evolution Images Using Intelligent Systems). In: Moreno-Díaz Jr., R., Pichler, F. (eds.) EUROCAST 2003. LNCS, vol. 2809, pp. 651–660. Springer, Heidelberg (2003)

    Google Scholar 

  3. Crespo, J.L., Bernardos, P., Zorrilla, M.E., Mora, E.: Meteorological Image Descriptors. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2005. LNCS, vol. 3643, pp. 101–110. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Crespo, J.L., Zorrilla, M.E., Bernardos, P., Mora, E.: A new image prediction model based on spatio-temporal techniques. The Visual Computer (2007), doi:10.1007/s00371-007-0114-y

    Google Scholar 

  5. Elnagar, A., Gupta, K.: Motion Prediction of Moving Objects Based on Autoregressive Model. IEEE Trans. on Systems, MAN, and Cybernetics — Part A: Systems and Humans 28(6), 803–810 (1998)

    Article  Google Scholar 

  6. Hsiao, Y.T., Chuang, C.L., Lu, Y.L., Jiang, J.A.: Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames. Image Vis. Comput. 24, 1123–1136 (2006)

    Article  Google Scholar 

  7. Kehtarnavaz, N., Griswold, N.: Establishing collision zones for obstacles moving with uncertainty. Computer Vision, Graphics and Image Processing 49(1), 95–103 (1990)

    Article  Google Scholar 

  8. Pece, A.E.C., Worrall, A.D.: A comparison between feature-based and EM-based contour tracking. Image Vis. Comput. 24(11), 1218–1232 (2006)

    Article  Google Scholar 

  9. Pfeifer, P.E., Deutsch, S.J.: A three-stage iterative procedure for space-time modeling. Thechnometrics 22(1), 35–47 (1980)

    Article  MATH  Google Scholar 

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Roberto Moreno Díaz Franz Pichler Alexis Quesada Arencibia

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© 2007 Springer-Verlag Berlin Heidelberg

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Crespo, J.L., Bernardos, P., Zorrilla, M.E., Mora, E. (2007). Synoptic Maps Forecast Using Spatio-temporal Models. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_7

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  • DOI: https://doi.org/10.1007/978-3-540-75867-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75866-2

  • Online ISBN: 978-3-540-75867-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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