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Computer Intensive Methods in Statistics pp 1–29Cite as

Bayesian Edge-Detection in Images via Changepoint Methods

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Part of the book series: Statistics and Computing ((SCO))

Abstract

The problem of edge-detection in images will be formulated as a statistical changepoint problem using a Bayesian approach. It will be shown that the Gibbs sampler provides an effective procedure for the required Bayesian calculations. The use of the method for “quick and dirty” image segmentation will be illustrated.

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References

  • Basseville, M. (1981), Edge detection using sequential methods for change in level - Part II: Sequential detection of change in mean, IEEE Transactions on Acoustics, Sound, and Signal Processing, ASSP-29, 3250.

    Google Scholar 

  • Booth, N. B., and Smith, A. F. M. (1982), A Bayesian Approach to Retrospective Identification of Change-points, Journal of Econometrics, 19, 7–22.

    Article  MathSciNet  MATH  Google Scholar 

  • Bouthemy, P. (1989), A maximum likelihood framework for determining moving edges, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11, 499=-511

    Google Scholar 

  • Broemelìng, L. D. (1974), Bayesian Inferences about a Changing Sequence of Random Variables, Communications in Statistics, Part A–Theory and Methods, 3, 243–255.

    Article  MATH  Google Scholar 

  • Canny, J. (1986), A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence,PAMI-8 679–698

    Google Scholar 

  • Chemoff, H., and Zacks, S. (1964). Estimating the Correct Mean of a Normal distribution which is subjected to a Change in Time, Annals of Mathematical Statistics, 35, 999–1018.

    Article  MathSciNet  Google Scholar 

  • Cooper, D. B., and Sung, F. P. (1984), Multiple-window parallel adaptive boundary finding in computer vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-5, 299–314

    Google Scholar 

  • Gelfand, A. E., and Smith, A. F. M. (1990), Sampling Based Approaches to Calculating Marginal Densities, Journal of the American Statistical Association, 85, 398–409.

    Article  MathSciNet  MATH  Google Scholar 

  • Geman, S., and Geman, D. (1984), Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 721–741

    Google Scholar 

  • Hinkley, D. V. (1970), Inference about the Changepoint in a Sequence of Random Variables, Biometrika, 57, 1–17

    Article  MathSciNet  MATH  Google Scholar 

  • Hinkley, D. V. (1971), Inference about the Change-point from Cumulative Sum Tests, Biometrika, 58, 509523

    Google Scholar 

  • Kashyap, R. L., and Eom, K.-B. (1989), Texture boundary detection based on the long correlation model, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11, 58–67

    Google Scholar 

  • Lu, Y., and Jain, R. C. (1989), Behavior of edges in scale space, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11, 337–356

    Google Scholar 

  • Mascarenhas, N. D. A., and Prado, L. O. C. (1980), A Bayesian approach to edge detection in images, IEEE Transactions on Automatic Control, AC-25, 36–43

    Google Scholar 

  • Pettitt, A. N. (1980), A simple Cumulative Sum type Statistic for the Changepoint Problem with Zero-One observations, Biometrika, 67, 79–84

    Article  MathSciNet  MATH  Google Scholar 

  • Ripley, B. D. (1987), Stochastic Simulation,Wiley, New York.

    Google Scholar 

  • Rosenfeld, A., and Kak, A. C. (1982), Digital Picture Processing, Academic Press, New York.

    Google Scholar 

  • Shaban, S. A. (1980), Change point problem and two-phase regression: an annotated bibliography, International Statistical Review, 48, 83–93

    MathSciNet  MATH  Google Scholar 

  • Smith, A. F. M. (1975), A Bayesian Approach to Inference about a Changepoint in a Sequence of Random Variables, Biometrika, 62, 407–416

    Article  MathSciNet  MATH  Google Scholar 

  • Stephens, D. A. (1990), Bayesian Edge Detection in Image Processing, Unpublished Ph.D. thesis, University of Nottingham.

    Google Scholar 

  • Stephens, D. A. (1991), Bayesian Retrospective Multiple Changepoint Identification, Submitted for publication to Applied Statistics.

    Google Scholar 

  • Torre, V., and Poggio, T. A. (1986), On edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 147–163

    Google Scholar 

  • Zacks, S. (1982), Classical and Bayesian approaches to the changepoint problem, Statistique et Analyse des Donnees, 1, 48–81

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, UK

    D. A. Stephens & A. F. M. Smith

Authors
  1. D. A. Stephens
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  2. A. F. M. Smith
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Editor information

Editors and Affiliations

  1. Institut für Statistik und Ökonometrie, Wirtschaftswissenschaftliche Fakultät, Humboldt-Universität zu Berlin, Spandauer Straße 1, O-1020, Berlin, Germany

    Wolfgang Härdle

  2. Institute de Statistique, Université Catholique de Louvain, B-1348, Louvain-la-Neuve, Belgium

    Léopold Simar

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© 1993 Springer-Verlag Berlin Heidelberg

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Stephens, D.A., Smith, A.F.M. (1993). Bayesian Edge-Detection in Images via Changepoint Methods. In: Härdle, W., Simar, L. (eds) Computer Intensive Methods in Statistics. Statistics and Computing. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52468-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-52468-4_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0677-9

  • Online ISBN: 978-3-642-52468-4

  • eBook Packages: Springer Book Archive

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