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Publications about Biological images
Result of the query in the list of publications :
2 Articles |
1 - Variational approximation for detecting point-like target problems. D. Graziani and G. Aubert. COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029, August 2010. Keywords : points detection, Biological images, divergence-measure fields.
@ARTICLE{COCV2010,
|
author |
= |
{Graziani, D. and Aubert, G.}, |
title |
= |
{Variational approximation for detecting point-like target problems}, |
year |
= |
{2010}, |
month |
= |
{August}, |
journal |
= |
{COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029}, |
url |
= |
{http://dx.doi.org/10.1051/cocv/2010029}, |
keyword |
= |
{points detection, Biological images, divergence-measure fields} |
} |
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. |
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2 - Detecting codimension-two objects in an image with Ginzburg-Landau models. G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005. Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@ARTICLE{laure-ijcv05,
|
author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
title |
= |
{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
year |
= |
{2005}, |
month |
= |
{November}, |
journal |
= |
{International Journal of Computer Vision}, |
volume |
= |
{65}, |
number |
= |
{1-2}, |
pages |
= |
{29-42}, |
pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
keyword |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
} |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
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Conference article |
1 - A new variational method to detect points in biological images. D. Graziani and L. Blanc-Féraud and G. Aubert. In ISBI'09, Org. IEEE International Symposium on Biomedical Imaging, Boston, USA, June 2009. Keywords : Biological images, points detection, Gamma-convergence.
@INPROCEEDINGS{GRAZIANI_ISBI2009,
|
author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
title |
= |
{A new variational method to detect points in biological images}, |
year |
= |
{2009}, |
month |
= |
{June}, |
booktitle |
= |
{ISBI'09}, |
organization |
= |
{IEEE International Symposium on Biomedical Imaging}, |
address |
= |
{Boston, USA}, |
url |
= |
{http://dx.doi.org/10.1109/ISBI.2009.5193301}, |
keyword |
= |
{Biological images, points detection, Gamma-convergence} |
} |
Abstract :
We propose a new variational method to isolate points in biological images. As points are fine structures they are difficult to detect by derivative operators computed in the noisy image. In this paper we propose to compute a vector field from the observed intensity so that its divergence explodes at points. As the image could contains spots but also noise and curves where the divergence also blows up, we propose to capture spots by introducing suitable energy whose minimizers are given by the points we want to detect. In order to provide numerical experiments we approximate this energy by means of a sequence of more treatable functionals by a Gamma-convergence approach. Results are shown on synthetic and biological images. |
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Technical and Research Report |
1 - Detecting Codimension-two Objects in an Image with Ginzburg-Landau Models. G. Aubert and J.F. Aujol and L. Blanc-Féraud. Research Report 5254, INRIA, France, July 2004. Keywords : Ginzburg-Landau model, Biological images, Segmentation, Partial differential equation.
@TECHREPORT{5254,
|
author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
title |
= |
{Detecting Codimension-two Objects in an Image with Ginzburg-Landau Models}, |
year |
= |
{2004}, |
month |
= |
{July}, |
institution |
= |
{INRIA}, |
type |
= |
{Research Report}, |
number |
= |
{5254}, |
address |
= |
{France}, |
url |
= |
{https://hal.inria.fr/inria-00070744}, |
pdf |
= |
{https://hal.inria.fr/file/index/docid/70744/filename/RR-5254.pdf}, |
ps |
= |
{https://hal.inria.fr/docs/00/07/07/44/PS/RR-5254.ps}, |
keyword |
= |
{Ginzburg-Landau model, Biological images, Segmentation, Partial differential equation} |
} |
Résumé :
Dans cet article, nous proposons a nouveau modèle mathématique pour détecter dans une image les singularités de codimension supérieure ou égale à deux. Cela signifie que nous voulons détecter des points dans des images 2-D, ou des points et des courbes dans des images 3-D. Nous nous inspirons des modèles de Ginzburg-Landau (GL). Ces derniers se sont révélés efficace pour modéliser de nombreux phénomènes physiques. Nous introduisons le modèle, nous énonçons ses propriétés mathématiques, et nous donnons des résultats expérimentaux illustrant les performances du modèle. |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its mathematical properties and give some experimental results demonstrating its capability. |
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