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Publications about Segmentation
Result of the query in the list of publications :
5 Articles |
1 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation.
H. Permuter and J.M. Francos and I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, April 2006.
Keywords : Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion.
@ARTICLE{permuter_pr06,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, |
| year |
= |
{2006}, |
| month |
= |
{April}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{39}, |
| number |
= |
{4}, |
| pages |
= |
{695--706}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2005.10.028}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, |
| keyword |
= |
{Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion} |
| } |
Abstract :
The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces and to compare the performance of these models
in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'colour' and 'structure' information in order to improve the classification performance. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance. |
|
2 - An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals.
G. Aubert and L. Blanc-Féraud and R. March. Nonlinear Analysis, 64: pages 1908-1930, 2006.
Keywords : Gamma Convergence, Finite Element, Segmentation.
@ARTICLE{laure-na05,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals}, |
| year |
= |
{2006}, |
| journal |
= |
{Nonlinear Analysis}, |
| volume |
= |
{64}, |
| pages |
= |
{1908-1930}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_laure-na05.pdf}, |
| keyword |
= |
{Gamma Convergence, Finite Element, Segmentation} |
| } |
Abstract :
We show the Gamma-convergence of a family of discrete functionals to the Mumford and Shah image segmentation functional.
The functionals of the family are constructed by modifying the elliptic approximating functionals proposed by Ambrosio and Tortorelli. The quadratic term of the energy related to the edges of the segmentation is replaced by a nonconvex functional. |
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3 - 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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4 - Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model.
G. Poggi and G. Scarpa and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, August 2005.
Keywords : Classification, Segmentation, Markov Fields.
@ARTICLE{ieeetgrs_05,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model}, |
| year |
= |
{2005}, |
| month |
= |
{August}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{43}, |
| number |
= |
{8}, |
| pages |
= |
{1901-1911}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/32001/01487647.pdf?tp=&arnumber=1487647&isnumber=32001}, |
| keyword |
= |
{Classification, Segmentation, Markov Fields} |
| } |
|
5 - Globally optimal regions and boundaries as minimum ratio weight cycles.
I. H. Jermyn and H. Ishikawa. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(10): pages 1075-1088, October 2001.
Keywords : Graph, Ratio, Cycle, Segmentation, Global minimum.
Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@ARTICLE{jermyn_tpami01,
|
| author |
= |
{Jermyn, I. H. and Ishikawa, H.}, |
| title |
= |
{Globally optimal regions and boundaries as minimum ratio weight cycles}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{23}, |
| number |
= |
{10}, |
| pages |
= |
{1075-1088}, |
| url |
= |
{http://dx.doi.org/10.1109/34.954599}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_tpami01.pdf}, |
| keyword |
= |
{Graph, Ratio, Cycle, Segmentation, Global minimum} |
| } |
Abstract :
We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,ldots), em and from the interior of the region (texture, homogeneity,ldots). We describe two polynomial-time digraph algorithms for finding the em global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 times 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. |
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3 PhD Thesis and Habilitations |
1 - Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes..
A. Baudour. PhD Thesis, Universite de Nice Sophia Antipolis, May 2009.
Keywords : imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale.
@PHDTHESIS{baudour2009,
|
| author |
= |
{Baudour, A.}, |
| title |
= |
{Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.}, |
| year |
= |
{2009}, |
| month |
= |
{May}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00507520/}, |
| keyword |
= |
{imagerie 3D, Segmentation, filaments, Deconvolution, Variational methods, mocroscopie confocale} |
| } |
Résumé :
Cette thèse aborde le problème de la modélisation et de la détection des laments
dans des images 3D.
Nous avons développé des méthodes variationnelles pour quatre applications
spéci ques :
l'extraction de routes où nous avons introduit la notion de courbure totale
pour conserver les réseaux réguliers en tolérant les discontinuités de
direction.
la détection et la complétion de laments fortement bruités et présentant
des occultation. Nous avons utilisé la magnétostatique et la théorie
de Ginzburg-Landau pour représenter les laments comme ensemble de
singularités d'un champ vectoriel.
la détection de laments dans des images biologiques acquises en microscopie
confocale. On modélise les laments en tenant compte des spéci cité
de cette dernière. Les laments sont alors obtenus par une méthode de
maximum à posteriori.
la détection de cible dans des séquences d'images infrarouges. Dans cette
application, on cherche des trajectoires optimisant la diérence de luminosit
é moyenne entre la trajectoire et son voisinage en tenant compte des
capteurs utilisés.
Par ailleurs, nous avons démontré des résultats théoriques portant sur la
courbure totale et la convergence de la méthode d'Alouges associée aux systèmes
de Ginzburg-Landau. Ce travail réunit à la fois modélisation, résulats théoriques
et recherche d'algorithmes numériques performants permettant de traiter de
réelles applications. |
|
2 - Indexing of satellite images using structural information.
A. Bhattacharya. PhD Thesis, Ecole Nationale Supérieure des Télécommunications, 2007.
Keywords : Landscape, Segmentation, Features, Extraction, Classification, Data mining.
@PHDTHESIS{bhattacharya_these,
|
| author |
= |
{Bhattacharya, A.}, |
| title |
= |
{Indexing of satellite images using structural information}, |
| year |
= |
{2007}, |
| school |
= |
{Ecole Nationale Supérieure des Télécommunications}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_bhattacharya_these.pdf}, |
| keyword |
= |
{Landscape, Segmentation, Features, Extraction, Classification, Data mining} |
| } |
|
3 - Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales.
A. Lorette. PhD Thesis, Universite de Nice Sophia Antipolis, September 1999.
Keywords : Texture, Segmentation, Markov Fields, Mathematical morphology, Urban areas.
@PHDTHESIS{lorette99,
|
| author |
= |
{Lorette, A.}, |
| title |
= |
{Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{Theses/these-lorette.pdf}, |
| keyword |
= |
{Texture, Segmentation, Markov Fields, Mathematical morphology, Urban areas} |
| } |
Résumé :
Dans cette thèse, nous nous intéressons au problème de l'analyse urbaine à partir d'images satellitales par des méthodes automatiques ou semi-automatiques issues du traitement d'image. Dans le premier chapitre, nous présentons le contexte dans lequel le travail a été effectué. Nous exposons les types de données utilisées, les approches statistiques considérées. Nous donnons également quelques exemples d'applications qui justifient une telle étude. Enfin, un état de l'art des diverses méthodes d'analyse des textures est présenté. Dans les deux chapitres suivants, nous développons une méthode automatique d'extraction d'un masque urbain à partir d'une analyse de la texture de l'image. Des méthodes d'extraction d'un masque urbain sont décrites. Ensuite, nous définissons plus précisemment les huit modèles markoviens gaussiens fondés sur des chaines. Ces modèles sont renormalisés par une méthode de renormalisation de groupe issue de la physique statistique afin de corriger le biais introduit par l'anisotropie du réseau de pixels. L'analyse de texture proposée est comparée avec deux méthodes classiques: les matrices de cooccurrence et les filtres de Gabor. L'image du paramètre de texture est ensuite classifiée avec un algorithme non supervisé de classification floue fondée sur la définition d'un critère entropique. Les paramètres estimés avec cet algorithme sont intégrés dans un modèle markovien de segmentation. Des résultats d'extraction de masques urbains sont finalement présentés sur des images satellitales optiques SPOT3, des simulations SPOT5, et des images radar ERS1. Dans le quatrième chapitre, nous présentons l'analyse granulométrique utilisée pour analyser le paysage urbain. Les outils et définitions de base de la morphologie mathématique sont exposés. Nous nous intéressons plus particulièrement à l'ouverture par reconstruction qui est utilisée comme transformation de base de la granulométrie. L'étape de quantification qui suit tout étape de transformation nous permet d'estimer en chaque pixel une distribution locale de taille qui est intégrée dans le terme d'attache aux données d'un modèle markovien de segmentation. Des tests sont effectués sur des simulations SPOT5. |
Abstract :
In this thesis, we investigate the problem of urban areas analysis from satellite images by automatic or semi-automatic methods coming from image processing. In the first chapter, we describe the context of this work, i.e. the type of used data, the statistical applied methods. We also give some examples of the applications which require such an analysis. Finally, a study of the existing methods of texture analysis is presented. In the second and third chapter, we develop a non supervised method based on texture analysis in order to extract an urban mask. First a study of the existing methods of urban mask extraction is presented. Second we precisely describe the eight chain-based Gaussian Markovian models used to characterize urban texture. These models are normalized through a renormalization group technique derived from statistical physics in order to correct the bias introduced by the anisotropy of the lattice.The above mentionned method of texture analysis is then compared with two classical ones: coocurrences matrix and Gabor filters. The image is then partitionned by an unsupervised fuzzy Cmeans algorithm based on an entropic criterion. The final segmentation is performed by the minimization of an energy derived from a Markovian model. Some results are presented that are obtained from SPOT3 images, SPOT5 simulations and radar ERS1 images. In the fourth chapter, we present the granulometric approach used to segment within the urban area itself. The basic operations and definitions of mathematical morphology are settled. We are particularly interested in opening by reconstruction operation based on geodesic dilatations. In fact this operation is used to define a granulometry. The quantification step that follows the transformation step consists in estimating a local size distribution function for each pixel. These parameters are then integrated in the data term of a Markovian model. Some results on SPOT5 simulations are presented. |
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10 Conference articles |
1 - Morphological road segmentation in urban areas from high resolution satellite images.
R. Gaetano and J. Zerubia and G. Scarpa and G. Poggi. In International Conference on Digital Signal Processing, Corfu, Greece, July 2011.
Keywords : Segmentation, Classification, skeletonization , pattern recognition, shape analysis.
@INPROCEEDINGS{GaetanoDSP,
|
| author |
= |
{Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, |
| title |
= |
{Morphological road segmentation in urban areas from high resolution satellite images}, |
| year |
= |
{2011}, |
| month |
= |
{July}, |
| booktitle |
= |
{International Conference on Digital Signal Processing}, |
| address |
= |
{Corfu, Greece}, |
| url |
= |
{http://hal.inria.fr/inria-00618222/fr/}, |
| keyword |
= |
{Segmentation, Classification, skeletonization , pattern recognition, shape analysis} |
| } |
Abstract :
High resolution satellite images provided by the last generation
sensors significantly increased the potential of almost
all the image information mining (IIM) applications related
to earth observation. This is especially true for the extraction
of road information, task of primary interest for many remote
sensing applications, which scope is more and more extended
to complex urban scenarios thanks to the availability of highly
detailed images. This context is particularly challenging due
to such factors as the variability of road visual appearence
and the occlusions from entities like trees, cars and shadows.
On the other hand, the peculiar geometry and morphology of
man-made structures, particularly relevant in urban areas, is
enhanced in high resolution images, making this kind of information
especially useful for road detection.
In this work, we provide a new insight on the use of morphological
image analysis for road extraction in complex urban
scenarios, and propose a technique for road segmentation
that only relies on this domain. The keypoint of the technique
is the use of skeletons as powerful descriptors for road objects:
the proposed method is based on an ad-hoc skeletonization
procedure that enhances the linear structure of road segments,
and extracts road objects by first detecting their skeletons
and then associating each of them with a region of the
image. Experimental results are presented on two different
high resolution satellite images of urban areas. |
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2 - Brain tumor vascular network segmentation from micro-tomography.
X. Descombes and F. Plouraboue and El Boustani Habdelhkim and Fonta Caroline and |LeDuc Geraldine and Serduc Raphael and Weitkamp Timm. In Internation Symposium of Biomedical Imaging (ISBI), Chicago, USA, April 2011.
Keywords : Segmentation, Markov random field, Tomography, Brain, vascular network.
Copyright : IEEE
@INPROCEEDINGS{isbi11,
|
| author |
= |
{Descombes, X. and Plouraboue, F. and Boustani Habdelhkim, El and Caroline, Fonta and Geraldine, |LeDuc and Raphael, Serduc and Timm, Weitkamp}, |
| title |
= |
{Brain tumor vascular network segmentation from micro-tomography}, |
| year |
= |
{2011}, |
| month |
= |
{April}, |
| booktitle |
= |
{Internation Symposium of Biomedical Imaging (ISBI)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/ISBI.2011.5872596}, |
| keyword |
= |
{Segmentation, Markov random field, Tomography, Brain, vascular network} |
| } |
Abstract :
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus characterized by their respective distribution of watershed region size interpreted as local vascular territories. |
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