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Moore–Penrose approach in the Hough transform framework

Author

Listed:
  • Beltrametti, M.C.
  • Sendra, J.R.
  • Sendra, J.
  • Torrente, M.

Abstract

Let F(x, a) be a real polynomial in two sets of variables, x and a, that is linear with respect to one of the variable sets, say a. In this paper, we deal with two of the main steps of the Hough transform framework for the pattern recognition technique to detect loci in images. More precisely, we present an algorithmic process, based on the Moore–Penrose pseudo-inverse, to provide a region of analysis in the parameter space. In addition, we state an upper bound for the sampling distance of the discretization of the parameter space region.

Suggested Citation

  • Beltrametti, M.C. & Sendra, J.R. & Sendra, J. & Torrente, M., 2020. "Moore–Penrose approach in the Hough transform framework," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:apmaco:v:375:y:2020:i:c:s0096300320300527
    DOI: 10.1016/j.amc.2020.125083
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    References listed on IDEAS

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    1. Sendra, J. Rafael & Sendra, Juana, 2017. "Computation of Moore–Penrose generalized inverses of matrices with meromorphic function entries," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 355-366.
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