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Contrast Marginalised Gradient Template Matching

  • Saleh Basalamah
  • Anil Bharath
  • Donald McRobbie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)

Abstract

This paper addresses a key problem in the detection of shapes via template matching: the variation of accumulator-space response with object-background contrast. By formulating a probabilistic model for planar shape location within an image or video frame, a vector-field filtering operation may be derived which, in the limiting case of vanishing noise, leads to the Hough-transform filters reported by Kerbyson & Atherton [5]. By further incorporating a model for contrast uncertainty, a contrast invariant accumulator space is constructed, in which local maxima provide an indication of the most probable locations of a sought planar shape. Comparisons with correlation matching, and Hough transforms employing gradient magnitude, binary and vector templates are presented. A key result is that a posterior density function for locating a shape marginalised for contrast uncertainty is obtained by summing the functions of the outputs of a series of spatially invariant filters, thus providing a route to fast parallel implementations.

Keywords

Probability Density Function Template Match Hough Transform Planar Shape Posterior Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ballard, D.H.: Generalising the Hough Transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)zbMATHCrossRefGoogle Scholar
  2. 2.
    Bharath, A.A., Huberson, C.J.: Obtaining medial responses from steerable filters. IEE Proceedings on Vision, Image and Signal Processing 146(5), 1087–1088 (1999)CrossRefGoogle Scholar
  3. 3.
    Evans, M., Swartz, T.: Approximating Integrals via Monte Carlo and Deterministic Methods. Oxford University Press, Oxford (2000)zbMATHGoogle Scholar
  4. 4.
    Gradshteyn, I.S., Ryzhik, I.M., Jeffrey, A.: Tables of Integrals, Series and Products, 5th edn. Academic Press, London (1994)Google Scholar
  5. 5.
    Kerbyson, J.H., Atherton, T.J.: Circle detection using hough transform filters. In: Image Analysis and its Applications (1995); IEE Conference Publication No. 410, pp. 370–374 (1995)Google Scholar
  6. 6.
    Lighthill, M.J.: Introduction to Fourier Analysis and Generalised Functions. Cambridge monographs on mechanics and applied mathematics. Cambridge University Press, Cambridge (1958)zbMATHGoogle Scholar
  7. 7.
    MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  8. 8.
    Petrou, M., Kadyrov, A.: Affine invariant features from the trace transform. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 30–44 (2004)CrossRefGoogle Scholar
  9. 9.
    Pizer, S.M., Burbeck, C.A., Coggins, J.M., Fritsch, D.S., Morse, B.S.: Object shape before boundary shape: Scale-space medial axes. Journal of Mathematical Imaging and Vision 4, 303–313 (1994)CrossRefGoogle Scholar
  10. 10.
    Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing 50(11) (November 2002)Google Scholar
  11. 11.
    Simoncelli, E.P., Adelson, E.H.: Noise removal via bayesian wavelet coring. In: Proceedings of the 1996 International Conference on Image Processing, September 1996, vol. 1, pp. 379–382 (1996)Google Scholar
  12. 12.
    Tovée, M.J.: An Introduction to the human visual system. Cambridge Press, Cambridge (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Saleh Basalamah
    • 1
  • Anil Bharath
    • 1
  • Donald McRobbie
    • 2
  1. 1.Dept. of BioengineeringImperial College LondonLondonUK
  2. 2.Imaging Sciences Dept.Charing Cross Hospital, Imperial CollegeLondonUK

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