Éditeur(s) : HAL CCSD
Résumé : International audience
Les écosystèmes forestiers des Caraïbes

hal-00634756
https://hal.archives-ouvertes.fr/hal-00634756

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
CAS Proceedings
GOSIER, France

hal-00634750
https://hal.archives-ouvertes.fr/hal-00634750

Éditeur(s) : HAL CCSD
Résumé : Weather stations are constantly provided with thousands of satellite images with a view to anticipating the progress of natural phenomenons. This constant flow ofinformation requires speed and a strict analysis. Consequently, it is absolutely necessary to implement systems capable of working night and day, in order to make assessments. In this article, we suggest a method relying on pattern recognition investigation techniques, such as segmentation and classification. These techniques will be used to locate and discriminate the banks of clouds coming from images picked up by the GOES geostationary satellite. Treatment of the cloud shapes which have been found can be carried out, using operations, such as mathematical morphology, so as to draw discriminating characteristics. Classifier systems are used in order to interpret the characteristics which have been obtained and to conduct cloud classification.
15th Meeting of the Caribbean Academy of Sciences
Gosier, Guadeloupe

hal-00524903
https://hal.archives-ouvertes.fr/hal-00524903

Éditeur(s) : HAL CCSD
Résumé : International audience
Les écosystèmes forestiers des Caraïbes

hal-00634757
https://hal.archives-ouvertes.fr/hal-00634757

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with image processing. This study takes place in a segmentation process based on texture analysis. We use the multifractal ap-proach to characterize the textures. More precisely we study a particular multi-fractal spectrum called the large deviation spectrum. We consider two statistical methods to numerically compute this spectrum. The resulting spectrum, com-puted by both methods over an image, is a one dimension spectrum. In the scope of this article, we extend these methods in order to obtain a two dimen-sions spectrum which could be assimilated to an image. This 2D spectrum al-lows a local characterization of the image singularities while a 1D spectrum is a global characterization. Moreover, the computation of the spectrum requires the use of a measure. We introduce here a pre processing based on the gradient to improve the measure. We show results on both synthetic and real world images. Finally, we remark that the resulting 2D spectrum is close to the resulting im-age of an edge detection process while edge detection using one dimension spectrum requires post processing methods. This statement will be used for fu-ture works.
SITIS Proceedings
Hammamet, Tunisia

hal-00602273
https://hal.archives-ouvertes.fr/hal-00602273

Éditeur(s) : HAL CCSD Springer
Signal Processing for Image Enhancement and Multimedia Processing

hal-00509497
https://hal.archives-ouvertes.fr/hal-00509497

Éditeur(s) : HAL CCSD
Résumé : International audience
We propose in this article a color image segmentation technique based on an optimization of the Mean Shift method. It consists in classifying clusters of data points of a digital image, it does not require any preliminary designation of the number of classes and of their centers. The Mean Shift method applied to a digital color image creates a new image made up of aggregates of points belonging to a finished number of color classes. The complexity of this method based on a global approach of the image is in O(NxN), N being the number of pixels of the image. Our idea consists in applying a change of scale to the image to be segmented, to reduce the quantity of information and, in using a median filter to decrease the number of colors to minimize the complexity of the latter. The comparative study which we present shows that the optimization which we proposed gives better, reliable results than the classic use of the Mean Shift method.
Complex, Intelligent and Software Intensive Systems, International Conference
Cracovie, Poland

hal-00508427
https://hal.archives-ouvertes.fr/hal-00508427
DOI : 10.1109/CISIS.2010.62

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.
CIARP Proceedings
Cancun, Mexico

hal-00602268
https://hal.archives-ouvertes.fr/hal-00602268

Éditeur(s) : HAL CCSD
Résumé : International audience
Dans cet article, on utilise une méthode de segmentation appliquée à la reconnaissance de végétaux dans des images. La segmentation fournit des groupes de pixels, et nous proposons d'utiliser des opérateurs d'agrégation pour ordonner ces groupes afin d'améliorer les performances du système de reconnaissance. En particulier, on utilise un opérateur totalement renforcé appelé triple pi. Les premiers tests sont réalisés et montrent que l'utilisation du triple pi améliore les résultats pour la structuration des groupes de pixels.
7ème Atelier Fouille de Données Complexes dans un processus d'extraction de connaissances - Complexité liée aux données multiples
Hammamet, Tunisia

hal-00509725
https://hal.archives-ouvertes.fr/hal-00509725

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
ICSP Proceedings
Beijing, China

hal-00602270
https://hal.archives-ouvertes.fr/hal-00602270