Results 1 
7 of
7
Effective component tree computation with application to pattern recognition in astronomical imaging
 in Proc. IEEE Int. Conf. Image Processing 2007
"... 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 ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
(Show Context)
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.
A quasilinear algorithm to compute the tree of shapes of nD images
"... Abstract. To compute the morphological selfdual representation of images, namely the tree of shapes, the stateoftheart algorithms do not have a satisfactory time complexity. Furthermore the proposed algorithms are only effective for 2D images and they are far from being simple to implement. That ..."
Abstract

Cited by 20 (12 self)
 Add to MetaCart
(Show Context)
Abstract. To compute the morphological selfdual representation of images, namely the tree of shapes, the stateoftheart algorithms do not have a satisfactory time complexity. Furthermore the proposed algorithms are only effective for 2D images and they are far from being simple to implement. That is really penalizing since a selfdual representation of images is a structure that gives rise to many powerful operators and applications, and that could be very useful for 3D images. In this paper we propose a simpletowrite algorithm to compute the tree of shapes; it works for nD images and has a quasilinear complexity when data quantization is low, typically 12 bits or less. To get that result, this paper introduces a novel representation of images that has some amazing properties of continuity, while remaining discrete. 1
Droogenbroeck, Mathematical Morphology—From Theory to Applications, chapter 12: Algorithms for Mathematical Morphology
 323–353, ISTE
, 2010
"... In this chapter, we deal with the very important implementation problem of various image analysis operators, filters and methods seen in previous chapters. In general, researchers like to present a novel operator through a mathematical description. However, this may not always be a simple task to tr ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
In this chapter, we deal with the very important implementation problem of various image analysis operators, filters and methods seen in previous chapters. In general, researchers like to present a novel operator through a mathematical description. However, this may not always be a simple task to translate this description into computer code.
Habilitation à Diriger des Recherches spécialité Traitement du Signal et des Images
"... Un scientifique manipulant des données de nature variée est très souvent bloqué par son outil logiciel. En effet, l’implémentation d’algorithmes dans les bibliothèques logicielles restreint généralement leur utilisation à un petit nombre de types de données. Réussir à s’affranchir de ces limitation ..."
Abstract
 Add to MetaCart
Un scientifique manipulant des données de nature variée est très souvent bloqué par son outil logiciel. En effet, l’implémentation d’algorithmes dans les bibliothèques logicielles restreint généralement leur utilisation à un petit nombre de types de données. Réussir à s’affranchir de ces limitations est non seulement intéressant en tant que tel mais présente également d’autres avantages. Les possibilités de l’outil sont décuplées en termes d’extensibilité et de réutilisabilité; mieux, l’outil devient un facilitateur pour explorer de nouvelles pistes de recherche. Nous proposons une solution pour obtenir des bibliothèques logicielles scientifiques ayant de telles propriétés. Cette solution est multiparadigmes, mêlant généricité, orientéobjet et déclaratif. Elle aboutit à un ensemble cohérent de types de données, d’outils et d’algorithmes, tout en préservant les performances attendues en calcul scientifique. Une réalisation effective dédiée au domaine du traitement d’images, la bibliothèque Milena, sera présentée. Elle nous servira à illustrer l’efficience de la solution proposée.
Author manuscript, published in "International Symposium on Mathematical Morphology, Uppsala: Sweden (2013)"
, 2013
"... Two applications of shapebased morphology: blood vessels segmentation and a generalization of constrained connectivity ..."
Abstract
 Add to MetaCart
(Show Context)
Two applications of shapebased morphology: blood vessels segmentation and a generalization of constrained connectivity
TO
"... onnected operators are filtering tools that act by merging elementary regions called flat zones. Connecting operators cannot create new contours nor modify their position. Therefore, they have very good contourpreservation properties and are capable of both lowlevel filtering and higherlevel obje ..."
Abstract
 Add to MetaCart
(Show Context)
onnected operators are filtering tools that act by merging elementary regions called flat zones. Connecting operators cannot create new contours nor modify their position. Therefore, they have very good contourpreservation properties and are capable of both lowlevel filtering and higherlevel object recognition. This article gives an overview on connected operators and their application to image and video filtering. There are two popular techniques used to create connected operators. The first one relies on a reconstruction process. The operator involves first a simplification step based on a “classical ” filter and then a reconstruction process. In fact, the reconstruction can be seen as a way to create a connected version of an arbitrary operator. The simplification effect is defined and limited by the first step. The examples we show include simplification in terms of size or contrast. The second strategy to define connected operators relies on a hierarchical regionbased representation of the input image, i.e., a tree, computed in an initial step. Then, the simplification is obtained by pruning the tree, and, third, the output image is constructed from the pruned tree. This article presents the most important trees that have been used to create connected operators and also discusses important families of simplification or pruning criteria. We also give a brief overview on efficient implementations of the reconstruction process and of tree construction. Finally, the