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Publications about Texture
Result of the query in the list of publications :
2 Articles |
1 - Geometric Feature Extraction by a Multi-Marked Point Process .
F. Lafarge and G. Gimel'farb and X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, September 2010.
Keywords : Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing.
@ARTICLE{pami09b_lafarge,
|
| author |
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{Lafarge, F. and Gimel'farb, G. and Descombes, X.}, |
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{Geometric Feature Extraction by a Multi-Marked Point Process }, |
| year |
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{2010}, |
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{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
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{1597-1609}, |
| url |
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{http://dx.doi.org/10.1109/TPAMI.2009.152}, |
| keyword |
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{Shape extraction, Spatial point process, Stochastic geometry, 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 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 |
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{A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, |
| year |
= |
{2006}, |
| month |
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{April}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{39}, |
| number |
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{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. |
|
 top of the page
2 PhD Thesis and Habilitations |
1 - Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes.
G. Rellier. PhD Thesis, Universite de Nice Sophia Antipolis, November 2002.
Keywords : Hyperspectral imaging, Texture, Classification, Markov Fields.
@PHDTHESIS{rellier,
|
| author |
= |
{Rellier, G.}, |
| title |
= |
{Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes}, |
| year |
= |
{2002}, |
| month |
= |
{November}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00505898}, |
| keyword |
= |
{Hyperspectral imaging, Texture, Classification, Markov Fields} |
| } |
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. 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 |
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{September}, |
| school |
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{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. |
|
top of the page
11 Conference articles |
1 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities.
K. Kayabol and A. Voisin and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, September 2011.
Keywords : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm.
@INPROCEEDINGS{inria-00592252,
|
| author |
= |
{Kayabol, K. and Voisin, A. and Zerubia, J.}, |
| title |
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{SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, |
| year |
= |
{2011}, |
| month |
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{September}, |
| booktitle |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| pages |
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{173-176}, |
| address |
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{Brussels, Belgium}, |
| url |
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{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 and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, September 2006.
Keywords : Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics.
@INPROCEEDINGS{aubray_eusipco06,
|
| author |
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{Aubray, J. and Jermyn, I. H. and Zerubia, J.}, |
| title |
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{Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, |
| year |
= |
{2006}, |
| month |
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{Proc. European Signal Processing Conference (EUSIPCO)}, |
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{Florence, Italy}, |
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| keyword |
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{Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, 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 and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Texture, Wavelet packet, Adaptive, Mother.
@INPROCEEDINGS{abhayaratne_icip05,
|
| author |
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{Texture-adaptive mother wavelet selection for texture analysis}, |
| year |
= |
{2005}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
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{Genoa, Italy}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, |
| keyword |
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{Texture, Wavelet packet, Adaptive, Mother} |
| } |
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 and I. H. Jermyn and J. Zerubia. In Proc. Physics in Signal and Image Processing, Toulouse, France, January 2005.
Keywords : Bimodal, Statistics, Wavelet packet, Adaptive, Texture.
@INPROCEEDINGS{cossu_psip05,
|
| author |
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{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
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{Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. Physics in Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, |
| keyword |
= |
{Bimodal, Statistics, Wavelet packet, Adaptive, 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 and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Bimodal, Adaptive, probabilistic, Wavelet packet, Texture.
@INPROCEEDINGS{cossu_icip04,
|
| author |
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{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
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{Texture discrimination using multimodal wavelet packet subbands}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
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{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, |
| keyword |
= |
{Bimodal, Adaptive, probabilistic, Wavelet packet, 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 and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics.
@INPROCEEDINGS{Abhayratne_icip04,
|
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| title |
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{Texture analysis using adaptative biorthogonal wavelet packets}, |
| year |
= |
{2004}, |
| month |
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| booktitle |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
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{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne04icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, 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. |
|
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. |
|
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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. |
|
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