Variational approximation for detecting point-like target problems

Gilles Aubert; Daniele Graziani

ESAIM: Control, Optimisation and Calculus of Variations (2011)

  • Volume: 17, Issue: 4, page 909-930
  • ISSN: 1292-8119

Abstract

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The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one.

How to cite

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Aubert, Gilles, and Graziani, Daniele. "Variational approximation for detecting point-like target problems." ESAIM: Control, Optimisation and Calculus of Variations 17.4 (2011): 909-930. <http://eudml.org/doc/272870>.

@article{Aubert2011,
abstract = {The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one.},
author = {Aubert, Gilles, Graziani, Daniele},
journal = {ESAIM: Control, Optimisation and Calculus of Variations},
keywords = {points detection; biological images; divergence-measure fields; p-capacity; Γ-convergence; point detection; 2-d biological images; -capacity; -convergence},
language = {eng},
number = {4},
pages = {909-930},
publisher = {EDP-Sciences},
title = {Variational approximation for detecting point-like target problems},
url = {http://eudml.org/doc/272870},
volume = {17},
year = {2011},
}

TY - JOUR
AU - Aubert, Gilles
AU - Graziani, Daniele
TI - Variational approximation for detecting point-like target problems
JO - ESAIM: Control, Optimisation and Calculus of Variations
PY - 2011
PB - EDP-Sciences
VL - 17
IS - 4
SP - 909
EP - 930
AB - The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one.
LA - eng
KW - points detection; biological images; divergence-measure fields; p-capacity; Γ-convergence; point detection; 2-d biological images; -capacity; -convergence
UR - http://eudml.org/doc/272870
ER -

References

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