Publications about Texture

Result of the query in the list of publications :

• 2 Articles
• 2 PhD Thesis and Habilitations
• 11 Conference articles
• 13 Technical and Research Reports

new search | get the bibtex

navigation : 1 - 2 - 3 display : 10 per page | 20 per page | 50 per page

11 Conference articles

7 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004. Keywords : Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics. @INPROCEEDINGS{cossu04a, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, year = {2004}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Montreal, Canada}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, keyword = {Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics} } Abstract : Although subband histograms of the wavelet coefficients of natural images possess a characteristic leptokurtotic form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, leptokurtotic, and interestingly, in some subbands, multimodal histograms. These subbands are closely linked to the structure of the texture, and guarantee that the most probable image is not flat. Motivated by these observations, we propose a probabilistic model that takes them into account. Adaptive wavelet packet subbands are modelled as Gaussian, generalized Gaussian, or a constrained Gaussian mixture. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling.

8 - Texture Analysis: An Adaptive Probabilistic Approach. K. Brady and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003. Keywords : Adaptive, Wavelet packet, Statistics, Texture. @INPROCEEDINGS{Brady03, author = {Brady, K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture Analysis: An Adaptive Probabilistic Approach}, year = {2003}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Barcelona, Spain}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03icip.pdf}, keyword = {Adaptive, Wavelet packet, Statistics, Texture} } Abstract : Two main issues arise when working in the area of texture segmentation: the need to describe the texture accurately by capturing its underlying structure, and the need to perform analyses on the boundaries of textures. Herein, we tackle these problems within a consistent probabilistic framework. Starting from a probability distribution on the space of infinite images, we generate a distribution on arbitrary finite regions by marginalization. For a Gaussian distribution, the computational requirement of diagonalization and the modelling requirement of adaptivity together lead naturally to adaptive wavelet packet models that capture the ‘significant amplitude features’ in the Fourier domain. Undecimated versions of the wavelet packet transform are used to diagonalize the Gaussian distribution efficiently, albeit approximately. We describe the implementation and application of this approach and present results obtained on several Brodatz texture mosaics.

9 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images. K. Brady and I. H. Jermyn and J. Zerubia. In Proc. British Machine Vision Conference (BMVC), Norwich, U. K., September 2003. Keywords : probabilistic, Adaptive, wavelet, Texture. @INPROCEEDINGS{Brady03a, author = {Brady, K. and Jermyn, I. H. and Zerubia, J.}, title = {Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images}, year = {2003}, month = {September}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Norwich, U. K.}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03bmvc.pdf}, keyword = {probabilistic, Adaptive, wavelet, Texture} } Abstract : Remote sensing imagery plays an important role in many elds. It has become an invaluable tool for diverse applications ranging from cartography to ecosystem management. In many of the images processed in these types of applications, semantic entities in the scene are correlated with textures in the image. In this paper, we propose a new method of analysing such textures based on adaptive probabilistic models of wavelet packets. Our approach adapts to the principal periodicities present in the textures, and can capture long-range correlations while preserving the independence of the wavelet packet coefcients. This technique has been applied to several remote sensing images, the results of which are presented.

10 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval. H. Permuter and J.M. Francos and I. H. Jermyn. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, April 2003. Keywords : Texture, Gaussian mixture, Classification, Aerial images. @INPROCEEDINGS{Permuter03, author = {Permuter, H. and Francos, J.M. and Jermyn, I. H.}, title = {Gaussian Mixture Models of Texture and Colour for Image Database Retrieval}, year = {2003}, month = {April}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Hong Kong}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Permuter03icassp.pdf}, keyword = {Texture, Gaussian mixture, Classification, Aerial images} } Abstract : We introduce Gaussian mixture models of ‘structure’ and colour features in order to classify coloured textures in images, with a view to the retrieval of textured colour images from databases. Classifications are performed separately using structure and colour and then combined using a confidence criterion. We apply the models to the VisTex database and to the classification of man-made and natural areas in aerial images. We compare these models with others in the literature, and show an overall improvement in performance.

11 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets. C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002. Keywords : Segmentation, Hidden Markov Model, Texture, Colour. @INPROCEEDINGS{ijking, author = {Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, title = {Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, year = {2002}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, keyword = {Segmentation, Hidden Markov Model, Texture, Colour} } Abstract : The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has emerged as an application area in which retrieval is attempted by trying to gain unsupervised access to the image semantics directly rather than via manual annotation. To this end, we present an unsupervised segmentation technique in which colour and texture models are learned from the image prior to segmentation, and whose output (including the models) may subsequently be used as a content descriptor in a CBIR system. These models are obtained in a multiresolution setting in which Hidden Markov Trees (HMT) are used to model the key statistical properties exhibited by complex wavelet and scaling function coefficients. The unsupervised Mean Shift Iteration (MSI) procedure is used to determine a number of image regions which are then used to train the models for each segmentation class.

top of the page

13 Technical and Research Reports

1 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model. K. Kayabol and J. Zerubia. Research Report 7700, INRIA, July 2011. Keywords : High resolution SAR, Classification, Texture. @TECHREPORT{Kayabol11, author = {Kayabol, K. and Zerubia, J.}, title = {Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, year = {2011}, month = {July}, institution = {INRIA}, type = {Research Report}, number = {7700}, url = {http://hal.archives-ouvertes.fr/hal-00612491/fr/}, keyword = {High resolution SAR, Classification, Texture} } 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 and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.

2 - Hierarchical finite-state modeling for texture segmentation with application to forest classification. G. Scarpa and M. Haindl and J. Zerubia. Research Report 6066, INRIA, INRIA, France, December 2006. Keywords : Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis. @TECHREPORT{scarparr06, author = {Scarpa, G. and Haindl, M. and Zerubia, J.}, title = {Hierarchical finite-state modeling for texture segmentation with application to forest classification}, year = {2006}, month = {December}, institution = {INRIA}, type = {Research Report}, number = {6066}, address = {INRIA, France}, url = {https://hal.inria.fr/inria-00118420}, keyword = {Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis} } Abstract : In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN).

3 - Models of the Unimodal and Multimodal Statistics of Adaptive Wavelet Packet Coefficients. R. Cossu and I. H. Jermyn and K. Brady and J. Zerubia. Research Report 5122, INRIA, France, February 2004. Keywords : Wavelet packet, Texture. @TECHREPORT{5122, author = {Cossu, R. and Jermyn, I. H. and Brady, K. and Zerubia, J.}, title = {Models of the Unimodal and Multimodal Statistics of Adaptive Wavelet Packet Coefficients}, year = {2004}, month = {February}, institution = {INRIA}, type = {Research Report}, number = {5122}, address = {France}, url = {https://hal.inria.fr/inria-00071461}, pdf = {https://hal.inria.fr/file/index/docid/71461/filename/RR-5122.pdf}, ps = {https://hal.inria.fr/docs/00/07/14/61/PS/RR-5122.ps}, keyword = {Wavelet packet, Texture} } Résumé : De récents travaux ont montré que bien que les histogrammes de sous-bandes pour les coefficients d'ondelettes standards ont une forme de gaussienne généralisée, ce n'est plus vrai pour les bases de paquets d'ondelettes adaptés à une certaine texture. Trois types de statistiques sont alors observés pour les sous-bandes: gaussienne, gaussienne generalisée et dans certaines sous-bandes des histogrammes multimodaux sans mode en zéro. Dans ce rapport, nous démontrons que ces sous-bandes sont étroitement liées à la structure de la texture et sont ainsi primordiales dans les applications dans lesquelles la texture joue un rôle important. Fort de ces observations, nous étendons l'approche de modélisation de textures proposée par en incluant ces sous-bandes. Nous modifions l'hypothèse gaussienne pour inclure les gaussiennes généralisées et les mixtures de gaussiennes contraintes. Nous utilisons une méthodologie bayésienne, définissant des estimateurs MAP pour la base adaptative, pour la sélection du modèle de la sous-bande et pour les paramètres de ce modèle. Les résultats confirment l'efficacité de la méthode proposée et soulignent l'importance des sous-bandes multimodales pour la discrimination et la modélisation de textures. Abstract : In recent work, it was noted that although the subband histograms for standard wavelet coefficients 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 most interestingly, in some subbands, multimodal histograms with no mode at zero. As will be demonstrated in this report, these latter subbands are closely linked to the structure of the texture, and are thus likely to be important for many applications in which texture plays a role. Motivated by these observations, we extend the approach to texture modelling proposed by to include these subbands. We relax the Gaussian assumption to include generalized Gaussians and constrained Gaussian mixtures. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling.

4 - Structure and Texture Compression. J.F. Aujol and B. Matei. Research Report 5076, INRIA, France, January 2004. Keywords : Bounded Variation Space, Image decomposition, Texture, Structure. @TECHREPORT{5076, author = {Aujol, J.F. and Matei, B.}, title = {Structure and Texture Compression}, year = {2004}, month = {January}, institution = {INRIA}, type = {Research Report}, number = {5076}, address = {France}, url = {https://hal.inria.fr/inria-00071507}, pdf = {https://hal.inria.fr/file/index/docid/71507/filename/RR-5076.pdf}, ps = {https://hal.inria.fr/docs/00/07/15/07/PS/RR-5076.ps}, keyword = {Bounded Variation Space, Image decomposition, Texture, Structure} } Résumé : Dans ce papier, nous nous intéressons au problème de la compression d'image. Les ondelettes se sont révélées être un outil particulièremment efficace . Récemment, de nombreux algorithmes ont été proposés pour amméliorer la compression par ondelettes en essayant de prendre en compte les strucutres présentes dans l'image. De telles méthodes se révèlents très efficaces pour les images géométriques. Nous construisons un algorithme de compression d'images qui prend en compte la géométrie de l'image tout en étant capable d'être performant sur des images contenant à la fois des structures et des textures. Pour cela, nous utilisons un algorithme de décomposition d'image récemment introduit dans . Cet algorithme permet de séparer une image en deux composantes, une première composante contenant l'information géométrique de l'image, et une deuxième contenant les éléments oscillants de l'image. L'idée de notre méthode de compression est la suivante. Nous commen ons par décomposer l'image à compresser en sa partie géométrique et sa partie oscillante. Nous effectuons ensuite la compression de la partie géométrique à l'aide de l'algorithme introduit dans , ce dernier étant particulièrement bien adapté pour la compression des structures d'une image. Pour la partie oscillante de l'image, nous utilisons l'algorithme classique de compression par ondelettes biorthogonales. sur les zones régulières d'une image). l'image. Notre nouvel algorithme de compression s'avère plus performant que la méthode classique par ondelettes biorthogonales. meilleurs à la fois en PSNR, et aussi visuellement (les bords sont plus précis et les textures sont mieux conservées). Abstract : In this paper, we tackle the problem of image compression. During the last past years, many algorithms have been proposed to take advantage of the geometry of the image. We intend here to propose a new compression algorithm which would take into account the structures in the image, and which would be powerful even when the original image has some textured areas. To this end, we first split our image into two components, a first one containing the structures of the image, and a second one the oscillating patterns. We then perform the compression of each component separately. Our final compressed image is the sum of these two compressed components. This new compression algorithm outperforms the standard biorthogonal wavelets compession.

5 - A Probabilistic Framework for Adaptive Texture Description. K. Brady and I. H. Jermyn and J. Zerubia. Research Report 4920, INRIA, France, September 2003. Keywords : Segmentation, Texture, Wavelet packet. @TECHREPORT{4920, author = {Brady, K. and Jermyn, I. H. and Zerubia, J.}, title = {A Probabilistic Framework for Adaptive Texture Description}, year = {2003}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {4920}, address = {France}, url = {https://hal.inria.fr/inria-00071659}, pdf = {https://hal.inria.fr/file/index/docid/71659/filename/RR-4920.pdf}, ps = {https://hal.inria.fr/docs/00/07/16/59/PS/RR-4920.ps}, keyword = {Segmentation, Texture, Wavelet packet} } Résumé : Ce rapport présente le développement d'un nouveau cadre probabiliste cohérent pour la description adaptative de texture. En partant d'une distribution de probabilité sur un espace d'images infinies, nous générons une distribution sur des régions finies par marginalisation. Pour une distribution gaussienne, les contraintes de calcul imposées par la diagonalisation nous conduisent naturellement à des modèles utilisant des paquets d'ondelettes adaptatifs. Ces modèles reflètent les principales périodicités présentes dans les textures et permettent également d'avoir des corrélations à longue portée tout en préservant l'indépendance des coefficients des paquets d'ondelettes. Nous avons appliqué notre méthode à la segmentation. Deux types de données figurent dans notre ensemble de test: des mosaïques synthétiques de Brodatz et des images satellitaires haute résolution. Dans le cas des textures synthétiques, nous utilisons la version non-décimée de la transformée en paquets d'ondelettes afin de diagonaliser la distribution gaussienne de manière efficace, bien qu'approximative. Cela nous permet d'effectuer une classification de la mosaique pixel par pixel. Une étape de régularisation est ensuite effectuée afin d'arriver à un résultat de segmentation final plus lisse. Afin d'obtenir les meilleurs résultats possibles dans le cas de données réelles, la moyenne de la distribution est ensuite introduite dans le modèle. L'approximation faite pour la classification des mosaiques de textures synthetiques a été testée sur des images réelles, mais les résultats obtenus n'étaient pas satisfaisants. C'est pourquoi nous avons introduit, pour ce type de données, une technique de classification heuristique basée sur la transformée en paquets d'ondelettes décimée. Les résultats de segmentation sont ensuite régularisés à l'aide de la même méthode que dans le cas synthétique. Nous présentons les résultats pour chaque type de données et concluons par une discussion. Abstract : This report details the development of a probabilistic framework for adaptive texture description. Starting with a probability distribution on the space of infinite images, we generate a distribution on finite regions by marginalisation. For a Gaussian distribution, the computational requirement of diagonalisation leads naturally to adaptive wavelet packet models which capture the principal periodicities present in the textures and allow long-range correlations while preserving the independence of the wavelet packet coefficients. These models are then applied to the task of segmentation. Two data types are included in our test bed: synthetic Brodatz mosaics and high-resolution satellite images. For the case of the synthetic textures, undecimated versions of the wavelet packet transform are used to diagonalise the Gaussian distribution efficiently, albeit approximately. This enables us to perform a pixelwise classification of the mosaics. A regularisation step is then implemented in order to arrive at a smooth final segmentation. In order to obtain the best possible results for the real dataset, the mean of the distribution is included in the model. The approximation made for the classification of the synthetic texture mosaics is tested on the remote sensing images, but it produces unsatisfactory results. Therefore we introduce a heuristic classification technique for this dataset, based on a decimated wavelet packet transform. The resulting segmentation is then regularised using the same method as in the synthetic case. Results are presented for both types of data and a discussion follows.

top of the page

navigation : 1 - 2 - 3 display : 10 per page | 20 per page | 50 per page

These pages were generated by