Abstract
In recent years, a number of statistical models have been proposed for the purposes of high-level image analysis tasks such as object recognition. However, in general, these models remain hard to use in practice, partly as a result of their complexity, partly through lack of software. In this paper we concentrate on a particular deformable template model which has proved potentially useful for locating and labelling cells in microscope slides Rue and Hurn (1999). This model requires the specification of a number of rather non-intuitive parameters which control the shape variability of the deformed templates. Our goal is to arrange the estimation of these parameters in such a way that the microscope user's expertise is exploited to provide the necessary training data graphically by identifying a number of cells displayed on a computer screen, but that no additional statistical input is required. In this paper we use maximum likelihood estimation incorporating the error structure in the generation of our training data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baddeley A. and Turner R. 2000. Practical maximum pseudolikelihood for spatial point patterns. Australia and New Zealand. Journal of Statistics 42: 283–322.
Baddeley A.J. and Van Lieshout M.N.M. 1993. Stochastic geometry models in high-level vision. In: Mardia K.V. and Kanji G.K. (Eds.), Statistics and Images, Vol. 20. Carfax Publishing, Abingdon, Chapter 11, pp. 235–256.
Blake A. and Isard M. 1998. Active Contours. Springer-Verlag, Berlin.
Dryden I. and Mardia K. 1999. Statistical Shape Analysis. Wiley, Chichester.
Gilks W.R., Richardson S., and Spiegelhalter D.J. 1996. Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
Grenander U., Chow Y., and Keenan D.M. 1991. Hands: A Pattern Theoretic Study of Biological Shapes, Research Notes on Neural Computing. Springer, Berlin.
Grenander U. and Miller M.I. 1994. Representations of knowledge in complex systems (with discussion). Journal of the Royal Statistical Society, Series B 56(4): 549–603.
Hurn M.A. 1998. Confocal fluorescence microscopy of leaf cells: An application of Bayesian image analysis. Journal of the Royal Statistical Society, Series C (47): 361–377.
Hurn M.A. and Rue H. 1997. High-level image priors in confocal microscopy applications. In: Mardia K.V., Gill C.A. and Aykroyd R. (Eds), The Art and Science of Bayesian Image Analysis. University of Leeds, Leeds, pp. 36–43.
Jain A.K., Zhong Y., and Lakshmanan S. 1996. Object matching using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(3): 267–278.
Kent J.T., Dryden I.L., and Anderson C.R. 2000. Using circulant symmetry to model featureless objects. Bometrika 87: 527–544.
Kent J.T., Mardia K.V., and Walder A.N. 1996. Conditional cyclic Markov random fields. Advances in Applied Probability (SGSA) 28: 1–12.
Rue H. and Hurn M.A. 1999. Bayesian object identification. Biometrika 86(3): 649–660.
The MathWorks Inc. 1994. MATLAB User's Guide, Version 4.2c. The Math Works Inc, Natick.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Hurn, M., Steinsland, I. & Rue, H. Parameter estimation for a deformable template model. Statistics and Computing 11, 337–346 (2001). https://doi.org/10.1023/A:1011921103843
Issue Date:
DOI: https://doi.org/10.1023/A:1011921103843