Publications sur Texture

Résultat de la recherche dans la liste des publications :

• 2 Articles
• 2 Thèses de Doctorat et Habilitations
• 11 Articles de conférence
• 13 Rapports de recherche et Rapports techniques

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2 Articles

1 - Geometric Feature Extraction by a Multi-Marked Point Process . F. Lafarge et G. Gimel'farb et X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, septembre 2010. Mots-clés : Shape extraction, Spatial point process, Geometrie stochastique, fast optimization, Texture, remote sensing. @ARTICLE{pami09b_lafarge, author = {Lafarge, F. and Gimel'farb, G. and Descombes, X.}, title = {Geometric Feature Extraction by a Multi-Marked Point Process }, year = {2010}, month = {septembre}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {32}, number = {9}, pages = {1597-1609}, url = {http://dx.doi.org/10.1109/TPAMI.2009.152}, keyword = {Shape extraction, Spatial point process, Geometrie stochastique, fast optimization, Texture, remote sensing} } Abstract : This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency.

2 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation. H. Permuter et J.M. Francos et I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, avril 2006. Mots-clés : Classification, Segmentation, Texture, Couleur, Mixture de gaussiennes, 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 = {avril}, 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, Couleur, Mixture de gaussiennes, 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.

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2 Thèses de Doctorat et Habilitations

1 - Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes. G. Rellier. Thèse de Doctorat, Universite de Nice Sophia Antipolis, novembre 2002. Mots-clés : Imagerie hyperspectrale, Texture, Classification, Champs de Markov. @PHDTHESIS{rellier, author = {Rellier, G.}, title = {Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes}, year = {2002}, month = {novembre}, school = {Universite de Nice Sophia Antipolis}, url = {https://hal.inria.fr/tel-00505898}, keyword = {Imagerie hyperspectrale, Texture, Classification, Champs de Markov} } Résumé : Dans cette thèse, on aborde le problème de l'analyse de texture pour l'étude des zones urbaines. La texture est une notion spatiale désignant ce qui, en dehors de la couleur ou du niveau de gris, caractérise l'homogénéité visuelle d'une zone donnée d'une image. Le but de cette étude est d'établir un modèle qui permette une analyse de texture prenant en compte conjointement l'aspect spatial et l'aspect spectral, à partir d'images hyperspectrales. Ces images sont caractérisées par un nombre de canaux largement supérieur à celui des images multispectrales classiques. On désire tirer parti de l'information spectrale pour améliorer l'analyse spatiale. Les textures sont modélisées par un champ de Markov gaussien vectoriel, qui permet de prendre en compte les relations spatiales entre pixels, mais aussi les relations inter-bandes à l'intérieur d'un même pixel. Ce champ est adapté aux images hyperspectrales par une simplification évitant l'apparition de problèmes d'estimation statistique dans des espaces de grande dimension. Dans le but d'éviter ces problèmes, on effectue également une réduction de dimension des données grâce à un algorithme de poursuite de projection. Cet algorithme permet de déterminer un sous-espace de projection dans lequel une grandeur appelée indice de projection est optimisée. L'indice de projection est défini par rapport à la modélisation de texture proposée, de manière à ce que le sous-espace optimal maximise la distance entre les classes prédéfinies, dans le cadre de la classification. La méthode d'analyse de texture est testée dans le cadre d'une classification supervisée. Pour ce faire, on met au point deux algorithmes que l'on compare avec des algorithmes classiques utilisant ou non l'information de texture. Des tests sont réalisés sur des images hyperspectrales AVIRIS. Abstract : In this work, we investigate the problem of texture analysis of urban areas. Texture is a spatial concept that refers to the visual homogeneity characteristics of an image, not taking into account color or grey level. The aim of this research is to define a model which allows a joint spectral and spatial analysis of texture, and then to apply this model to hyperspectral images. These images many more bands than classical multispectral images. We intend to make use of spectral information and improve simple spatial analysis. Textures are modeled by a vectorial Gauss-Markov random field, which allows us to take into account the spatial interactions between pixels as well as inter-band relationships for a single pixel. This field has been adapted to hyperspectral images by a simplification which avoids statistical estimation problems common to high dimensional spaces. In order to avoid these problems, we also reduce the dimensionality of the data, using a projection pursuit algorithm. This algorithm determines a projection subspace in which an index, called projection index, is optimized. This index is defined in relation to the proposed texture model so that, when a classification is being carried out, the optimal subspace maximizes the distance between predefined training samples. This texture analysis method is tested within a supervised classification framework. For this purpose, we propose two classification algorithms that we compare to two classical algorithms, one which uses texture information and one which does not. Tests are carried out on AVIRIS hyperspectral images.

2 - Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales. A. Lorette. Thèse de Doctorat, Universite de Nice Sophia Antipolis, septembre 1999. Mots-clés : Texture, Segmentation, Champs de Markov, Morphologie mathematique, Zones urbaines. @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 = {septembre}, school = {Universite de Nice Sophia Antipolis}, pdf = {Theses/these-lorette.pdf}, keyword = {Texture, Segmentation, Champs de Markov, Morphologie mathematique, Zones urbaines} } 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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11 Articles de conférence

1 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities. K. Kayabol et A. Voisin et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, septembre 2011. Mots-clés : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm. @INPROCEEDINGS{inria-00592252, author = {Kayabol, K. and Voisin, A. and Zerubia, J.}, title = {SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, year = {2011}, month = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, pages = {173-176}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592252/en/}, keyword = {High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

2 - Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture. J. Aubray et I. H. Jermyn et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, septembre 2006. Mots-clés : Texture, Adaptatif, Paquet d'ondelettes, Nonlineaire, Bimodale, Statistics. @INPROCEEDINGS{aubray_eusipco06, author = {Aubray, J. and Jermyn, I. H. and Zerubia, J.}, title = {Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, year = {2006}, month = {septembre}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Florence, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, keyword = {Texture, Adaptatif, Paquet d'ondelettes, Nonlineaire, Bimodale, Statistics} } Abstract : Probabilistic adaptive wavelet packet models of texture pro- vide new insight into texture structure and statistics by focus- ing the analysis on significant structure in frequency space. In very adapted subbands, they have revealed new bimodal statistics, corresponding to the structure inherent to a texture, and strong dependencies between such bimodal sub- bands, related to phase coherence in a texture. Existing models can capture the former but not the latter. As a first step to- wards modelling the joint statistics, and in order to simplify earlier approaches, we introduce a new parametric family of models capable of modelling both bimodal and unimodal subbands, and of being generalized to capture the joint statistics. We show how to compute MAP estimates for the adaptive basis and model parameters, and apply the models to Brodatz textures to illustrate their performance.

3 - Texture-adaptive mother wavelet selection for texture analysis. G.C.K. Abhayaratne et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, septembre 2005. Mots-clés : Texture, Paquet d'ondelettes, Adaptatif, Mere. @INPROCEEDINGS{abhayaratne_icip05, author = {Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture-adaptive mother wavelet selection for texture analysis}, year = {2005}, month = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, keyword = {Texture, Paquet d'ondelettes, Adaptatif, Mere} } Abstract : Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length.

4 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory. R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. Physics in Signal and Image Processing, Toulouse, France, janvier 2005. Mots-clés : Bimodale, Statistics, Paquet d'ondelettes, Adaptatif, Texture. @INPROCEEDINGS{cossu_psip05, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, year = {2005}, month = {janvier}, booktitle = {Proc. Physics in Signal and Image Processing}, address = {Toulouse, France}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, keyword = {Bimodale, Statistics, Paquet d'ondelettes, Adaptatif, Texture} } Abstract : In recent work, it was noted that although the subband histograms for standard wavelet coefcients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and interestingly, in some subbands, bi- or multi-modal histograms. Motivated by this observation, we provide additional experimental conrmation of the existence of multimodal subbands, and provide a theoretical explanation for their occurrence. The results reveal the connection of such subbands with the characteristic structure in a texture, and thus confirm the importance of such subbands for image modelling and applications.

5 - Texture discrimination using multimodal wavelet packet subbands. R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Singapore, octobre 2004. Mots-clés : Bimodale, Adaptatif, probabilistic, Paquet d'ondelettes, Texture. @INPROCEEDINGS{cossu_icip04, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Texture discrimination using multimodal wavelet packet subbands}, year = {2004}, month = {octobre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Singapore}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, keyword = {Bimodale, Adaptatif, probabilistic, Paquet d'ondelettes, Texture} } Abstract : The subband histograms of wavelet packet bases adapted to individual texture classes often fail to display the leptokurtotic behaviour shown by the standard wavelet coefcients of `natural' images. While many subband histograms remain leptokurtotic in adaptive bases, some subbands are Gaussian. Most interestingly, however, some subbands show multimodal behaviour, with no mode at zero. In this paper, we provide evidence for the existence of these multimodal subbands and show that they correspond to narrow frequency bands running throughout images of the texture. They are thus closely linked to the texture's structure. As such, they seem likely to possess superior descriptive and discriminative power as compared to unimodal subbands. We demonstrate this using both Brodatz and remote sensing images.

6 - Texture analysis using adaptative biorthogonal wavelet packets. G.C.K. Abhayaratne et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Singapore, octobre 2004. Mots-clés : Adaptatif, Paquet d'ondelettes, Biorthogonal, Texture, Statistics. @INPROCEEDINGS{Abhayratne_icip04, author = {Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture analysis using adaptative biorthogonal wavelet packets}, year = {2004}, month = {octobre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Singapore}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne04icip.pdf}, keyword = {Adaptatif, Paquet d'ondelettes, Biorthogonal, Texture, Statistics} } Abstract : We discuss the use of adaptive biorthogonal wavelet packet bases in a probabilistic approach to texture analysis, thus combining the advantages of biorthogonal wavelets (FIR, linear phase) with those of a coherent texture model. The computation of the probability uses both the primal and dual coefcients of the adapted biorthogonal wavelet packet basis. The computation of the biorthogonal wavelet packet coefcients is done using a lifting scheme, which is very efficient. The model is applied to the classification of mosaics of Brodatz textures, the results showing improvement over the performance of the corresponding orthogonal wavelets.

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