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Iterative curve organisation with the EM algorithm

  • Vision and AI Applications
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Advances in Artificial Intelligence (SBIA 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1159))

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Abstract

This paper describes how the early visual process of contour organisation can be realised using the EM algorithm of Dempster, Laird and Rubin [2]. The underlying computational representation is based on Zucker's idea of fine spline coverings [17]. According to our EM approach the adjustment of spline parameters draws on an iterative weighted least-squares fitting process. The expectation step of our EM procedure computes the likelihood of the data using a mixture model defined over the set of spline coverings. These splines are limited in their spatial extent using Gaussian windowing functions. The maximisation of the likelihood leads to a set of linear equations in the spline parameters which solve the weighted least squares problem. We evaluate the technique on the localisation of road structures in aerial infra-red images.

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Authors and Affiliations

  1. Department of Computer Science, University of York, Y01 5DD, York, UK

    J. A. F. Leite & E. R. Hancock

Authors
  1. J. A. F. Leite
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  2. E. R. Hancock
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Editor information

Díbio L. Borges Celso A. A. Kaestner

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© 1996 Springer-Verlag Berlin Heidelberg

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Leite, J.A.F., Hancock, E.R. (1996). Iterative curve organisation with the EM algorithm. In: Borges, D.L., Kaestner, C.A.A. (eds) Advances in Artificial Intelligence. SBIA 1996. Lecture Notes in Computer Science, vol 1159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61859-7_15

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  • DOI: https://doi.org/10.1007/3-540-61859-7_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61859-1

  • Online ISBN: 978-3-540-70742-4

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