A formal $\Gamma$-convergence approach for the detection of points in $2$-D images

Daniele Graziani; Laure Blanc-Féraud; Gilles Aubert

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A formal $\Gamma$-convergence approach for the detection of points in $2$-D images

Daniele Graziani ¹ Laure Blanc-Féraud ¹ Gilles Aubert ²

1 ARIANA - Inverse problems in earth monitoring

CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes

2 JAD - Laboratoire Jean Alexandre Dieudonné

Abstract : We propose a new variational model to isolate points in a $2$-dimensional image. To this purpose we introduce a suitable functional whose minimizers are given by the points we want to detect. In order to provide numerical experiments we replace this energy with a sequence of a more treatable functionals by means of the notion of $\Gamma$-convergence.

Keywords : biological $2$-D images points detection curvature-depending functionals divergence-measure fields $\Gamma$-convergence biological $2$-D images.

Document type :

Reports

Domain :

Computer Science [cs] / Image Processing [eess.IV]

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Contributor : Daniele Graziani <>
Submitted on : Saturday, September 19, 2009 - 4:41:35 PM
Last modification on : Tuesday, December 8, 2020 - 9:40:05 AM
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A formal $\Gamma$-convergence approach for the detection of points in $2$-D images

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A formal $\Gamma$-convergence approach for the detection of points in $2$-D images

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