Anthony Baillard

Web services at TERAPIX

By Olivier Ricou, Anthony Baillard, Emmanuel Bertin, Frederic Magnard, Chiara Marmo, Yannick Mellier

2007-09-23

In Proceedings of the XVII conference on astronomical data analysis software & systems (ADASS)

Abstract We present an implementation of V.O.-compliant web services built around software tools developed at the TERAPIX centre. These services allow to operate from a remote site several pipeline tasks dedicated to astronomical data processing on the TERAPIX cluster, including the latest EFIGI morphological analysis tool.

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Algorithme de calcul de l’arbre des composantes avec applications à la reconnaissance des formes en imagerie satellitaire

By Anthony Baillard, Christophe Berger, Emmanuel Bertin, Thierry Géraud, Roland Levillain, Nicolas Widynski

2007-05-11

In Proceedings of the 21st symposium on signal and image processing (GRETSI)

Abstract In this paper a new algorithm to compute the component tree is presented. As compared to the state-of-the-art, this algorithm does not use excessive memory and is able to work efficiently on images whose values are highly quantized or even with images having floating values. We also describe how it can be applied to astronomical data to identify relevant objects.

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Effective component tree computation with application to pattern recognition in astronomical imaging

By Christophe Berger, Thierry Géraud, Roland Levillain, Nicolas Widynski, Anthony Baillard, Emmanuel Bertin

2007-05-03

In Proceedings of the IEEE international conference on image processing (ICIP)

Abstract In this paper a new algorithm to compute the component tree is presented. As compared to the state of the art, this algorithm does not use excessive memory and is able to work efficiently on images whose values are highly quantized or even with images having floating values. We also describe how it can be applied to astronomical data to identify relevant objects.

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Project EFIGI: Automatic classification of galaxies

Abstract We propose an automatic system to classify images of galaxies with varying resolution. Morphologically typing galaxies is a difficult task in particular for distant galaxies convolved by a point-spread function and suffering from a poor signal-to-noise ratio. In the context of the first phase of the project EFIGI (extraction of the idealized shapes of galaxies in imagery), we present the three steps of our software: cleaning, dimensionality reduction and supervised learning.

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