Moore-Penrose approach in the Hough transform framework
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/55313DOI: 10.1016/j.amc.2020.125083
ISSN: 0096-3003
Publisher
Elsevier
Date
2020-06-15Funders
Agencia Estatal de Investigación
Bibliographic citation
Beltrametti, M.C., Sendra, J.R., Sendra, J. & Torrente, M. 2020, “Moore-Penrose approach in the Hough transform framework”, Applied Mathematics and Computation, vol. 375, art. no. 125083.
Keywords
Mutivariate polynomial
Pseudo-inverse matrix
Perturbed system
Hough transform
Parameter region detection
Parameter region discretization
Description / Notes
Maria-Laura Torrente is a member of GNAMPA - Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni of INDAM. J.R. Sendra is member of the Research Group ASYNACS (Ref.CT-CE2019/683).
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-88796-P/ES/COMPUTACION SIMBOLICA: NUEVOS RETOS EN ALGEBRA Y GEOMETRIA Y SUS APLICACIONES/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1016/j.amc.2020.125083Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2020 Elsevier
Access rights
info:eu-repo/semantics/openAccess
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.
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