A variational algorithm for the detection of line segments

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May 2014, 8(2): 389-408. doi: 10.3934/ipi.2014.8.389

A variational algorithm for the detection of line segments

Elena Beretta ^1, , Markus Grasmair ^2, , Monika Muszkieta ^3, and Otmar Scherzer ^4,

1.	Dipartimento di Matematica "Francesco Brioschi", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2.	Norwegian University of Science and Technology, 7491 Trondheim, Norway
3.	Institute of Mathematics and Computer Science, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
4.	Computational Science Center, University of Vienna, Nordbergstrasse 15, 1090 Wien

Received June 2013 Revised February 2014 Published May 2014

Abstract
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In this paper we propose an algorithm for the detection of edges in images that is based on topological asymptotic analysis. Motivated from the Mumford--Shah functional, we consider a variational functional that penalizes oscillations outside some approximate edge set, which we represent as the union of a finite number of thin strips, the width of which is an order of magnitude smaller than their length. In order to find a near optimal placement of these strips, we compute an asymptotic expansion of the functional with respect to the strip size. This expansion is then employed for defining a (topological) gradient descent like minimization method. As opposed to a recently proposed method by some of the authors, which uses coverings with balls, the usage of strips includes some directional information into the method, which can be used for obtaining finer edges and can also result in a reduction of computation times.

Keywords: topological minimization, Line segment detection, image segmentation..

Mathematics Subject Classification: Primary: 35C20; Secondary: 65K1.

Citation: Elena Beretta, Markus Grasmair, Monika Muszkieta, Otmar Scherzer. A variational algorithm for the detection of line segments. Inverse Problems & Imaging, 2014, 8 (2) : 389-408. doi: 10.3934/ipi.2014.8.389

References:

[1]	S. Amstutz, I. Horchani and M. Masmoudi, Crack detection by the topological gradient method,, Control and Cybernetics, 34 (2005), 81. Google Scholar
[2]	S. Amstutz, Sensitivity analysis with respect to a local perturbation of the material property,, Asymptotic Analysis, 49 (2006), 87. Google Scholar
[3]	L. Belaid, M. Jaoua, M. Masmoudi and L. Siala, Image restoration and edge detection by topological asymptotic expansion,, C. R. Acad. Sci. Paris, 342* (2006), 313. doi: 10.1016/j.crma.2005.12.009. Google Scholar*
[4]	E. Beretta, Y. Capdeboscq, F. de Gournay and E. Francini, Thin cylindrical conductivity inclusions in a three-dimensional domain: a polarization tensor and unique determination from boundary data., Inverse Probl., 25 (2009). doi: 10.1088/0266-5611/25/6/065004. Google Scholar
[5]	A. Braides, Approximation of Free-Discontinuity Problems, volume 1694 of Lecture Notes in Mathematics,, Springer-Verlag, (1998). Google Scholar
[6]	Y. Capdeboscq and M. S. Vogelius, A general representation formula for boundary voltage perturbations caused by internal conductivity inhomogeneities of low volume fraction,, M2AN Math. Model. Numer. Anal., 37 (2003), 159. doi: 10.1051/m2an:2003014. Google Scholar
[7]	Y. Capdeboscq and M. S. Vogelius, Pointwise polarization tensor bounds, and applications to voltage perturbations caused by thin inhomogeneities,, Asymptot. Anal., 50 (2006), 175. Google Scholar
[8]	G. Cortesani, Strong approximation of GSBV functions by piecewise smooth functions,, Ann. Univ. Ferrara Sez. VII (N.S.), 43 (1997), 27. Google Scholar
[9]	G. Cortesani and R. Toader, A density result in SBV with respect to non-isotropic energies,, Nonlinear Anal., 38 (1999), 585. doi: 10.1016/S0362-546X(98)00132-1. Google Scholar
[10]	G. Dong, M. Grasmair, S. H. Kang and O. Scherzer, Scale and edge detection with topological derivatives of the Mumford-Shah functional,, In A. Kuijper, (7893), 404. Google Scholar
[11]	D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order,, Reprint of the 2nd ed. Springer, (2001). Google Scholar
[12]	M. Grasmair, M. Muszkieta and O. Scherzer, An approach to the minimization of the Mumford-Shah functional using $\Gamma$-convergence and topological asymptotic expansion,, Interfaces Free Bound., 15 (2013), 141. doi: 10.4171/IFB/298. Google Scholar
[13]	Y. M. Jung, S. H. Kang and J. Shen, Multiphase image segmentation via Modica-Mortola phase transition,, SIAM J. Appl. Math., 67 (2007), 1213. doi: 10.1137/060662708. Google Scholar
[14]	O. A. Ladyzhenskaya and N. N. Ural'tseva, Linear and Quasilinear Elliptic Equations,, Translated from the Russian by Scripta Technica, (1968). Google Scholar
[15]	D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems,, Comm. Pure Appl. Math., 42 (1989), 577. doi: 10.1002/cpa.3160420503. Google Scholar
[16]	M. S. Vogelius and D. Volkov, Asymptotic formulas for perturbations in the electromagnetic fields due to the presence of inhomogeneities of small diameter,, M2AN Math. Model. Numer. Anal., 34 (2000), 723. doi: 10.1051/m2an:2000101. Google Scholar

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References:

[1]	S. Amstutz, I. Horchani and M. Masmoudi, Crack detection by the topological gradient method,, Control and Cybernetics, 34 (2005), 81. Google Scholar
[2]	S. Amstutz, Sensitivity analysis with respect to a local perturbation of the material property,, Asymptotic Analysis, 49 (2006), 87. Google Scholar
[3]	L. Belaid, M. Jaoua, M. Masmoudi and L. Siala, Image restoration and edge detection by topological asymptotic expansion,, C. R. Acad. Sci. Paris, 342* (2006), 313. doi: 10.1016/j.crma.2005.12.009. Google Scholar*
[4]	E. Beretta, Y. Capdeboscq, F. de Gournay and E. Francini, Thin cylindrical conductivity inclusions in a three-dimensional domain: a polarization tensor and unique determination from boundary data., Inverse Probl., 25 (2009). doi: 10.1088/0266-5611/25/6/065004. Google Scholar
[5]	A. Braides, Approximation of Free-Discontinuity Problems, volume 1694 of Lecture Notes in Mathematics,, Springer-Verlag, (1998). Google Scholar
[6]	Y. Capdeboscq and M. S. Vogelius, A general representation formula for boundary voltage perturbations caused by internal conductivity inhomogeneities of low volume fraction,, M2AN Math. Model. Numer. Anal., 37 (2003), 159. doi: 10.1051/m2an:2003014. Google Scholar
[7]	Y. Capdeboscq and M. S. Vogelius, Pointwise polarization tensor bounds, and applications to voltage perturbations caused by thin inhomogeneities,, Asymptot. Anal., 50 (2006), 175. Google Scholar
[8]	G. Cortesani, Strong approximation of GSBV functions by piecewise smooth functions,, Ann. Univ. Ferrara Sez. VII (N.S.), 43 (1997), 27. Google Scholar
[9]	G. Cortesani and R. Toader, A density result in SBV with respect to non-isotropic energies,, Nonlinear Anal., 38 (1999), 585. doi: 10.1016/S0362-546X(98)00132-1. Google Scholar
[10]	G. Dong, M. Grasmair, S. H. Kang and O. Scherzer, Scale and edge detection with topological derivatives of the Mumford-Shah functional,, In A. Kuijper, (7893), 404. Google Scholar
[11]	D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order,, Reprint of the 2nd ed. Springer, (2001). Google Scholar
[12]	M. Grasmair, M. Muszkieta and O. Scherzer, An approach to the minimization of the Mumford-Shah functional using $\Gamma$-convergence and topological asymptotic expansion,, Interfaces Free Bound., 15 (2013), 141. doi: 10.4171/IFB/298. Google Scholar
[13]	Y. M. Jung, S. H. Kang and J. Shen, Multiphase image segmentation via Modica-Mortola phase transition,, SIAM J. Appl. Math., 67 (2007), 1213. doi: 10.1137/060662708. Google Scholar
[14]	O. A. Ladyzhenskaya and N. N. Ural'tseva, Linear and Quasilinear Elliptic Equations,, Translated from the Russian by Scripta Technica, (1968). Google Scholar
[15]	D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems,, Comm. Pure Appl. Math., 42 (1989), 577. doi: 10.1002/cpa.3160420503. Google Scholar
[16]	M. S. Vogelius and D. Volkov, Asymptotic formulas for perturbations in the electromagnetic fields due to the presence of inhomogeneities of small diameter,, M2AN Math. Model. Numer. Anal., 34 (2000), 723. doi: 10.1051/m2an:2000101. Google Scholar

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