The Visual Computer

, Volume 23, Issue 6, pp 419–431 | Cite as

A new image prediction model based on spatio-temporal techniques

  • José Luis Crespo
  • Marta Zorrilla
  • Pilar Bernardos
  • Eduardo Mora
Original Article

Abstract

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images.

The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem.

As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.

Keywords

Causal images Spatio-temporal autoregressive model Tracking and prediction Image sequence Compression 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • José Luis Crespo
    • 1
  • Marta Zorrilla
    • 1
  • Pilar Bernardos
    • 1
  • Eduardo Mora
    • 1
  1. 1.Applied Mathematics and Computer Science DepartmentUniversity of CantabriaSantanderSpain

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