2021 |
Yahia, S.; Salem, Y. B.; Abdelkrim, M. N. 3D Textures Analysis Based on Features Extraction Article de journal Dans: Smart Sensors, Measurement and Instrumentation, vol. 40, p. 231-256, 2021, ISSN: 21948402, (cited By 1). Résumé | Liens | BibTeX | Étiquettes: 3d face analyse; 3D faces; 3d magnetic resonance brain image; Decimal descriptor pattern; Descriptors; Face analysis; Gray-level co-occurrence matrix; Grey-level co-occurrence matrixes; Local binary patterns; Magnetic resonance brain images; Support vectors machine, Brain mapping; Image analysis; Image texture; Magnetic resonance; Support vector machines, Textures @article{Yahia2021231,The Decimal Descriptor Patterns (DDP) operator is a recent method of analyzing textures. Based on structural analysis of pattern distribution, the DDP presents an excellent description of the spatial structure of image texture. In this chapter, two successful applications of this operator are highlighted. The first one consists in Multiple Sclerosis (MS) Lesions detection from 3D Magnetic Resonance (MR) brain images tissue. The second application is 3D face analysis. Different experimentations conducted on a two well-know three-dimensional databases. For comparison, two best known operators of texture measures that are considered as references in image analysis are chosen: the Grey Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). Several tests of texture classification are performed in the same conditions using the classifier multiclass Support Vector Machines (SVM). The performance of the three operators is tested in front of illumination, inhomogeneity, contrast, and noise level variations with a various and a large number of textures. Experimental results show clearly the robustness of the DDP operator. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
2019 |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Automation, Classification (of information), Extraction, Features extraction, GLCM, High spatial resolution, Image segmentation, Land cover classification, NDVI, Overall accuracies, Process control, Sentinel 2, supervised classification, Textures @conference{Chairet2019497b,Since 2015, the Sentinel 2 satellite provides a medium to high spatial resolution (10m-30m) images. For studying the land cover of Gabes area, located in the South- East of Tunisia, we exploited the 10 m bands of this satellite. We have tested the supervised classification with the SVM classifier. The classification is preceded by a segmentation step. The spectral data, the vegetation index and the texture metrics (GLCM) are used for training. The best Overall Accuracy OA (92, 12%) is obtained when all the used features are combined. © 2019 IEEE. |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). Résumé | Liens | BibTeX | Étiquettes: Automation, Classification (of information), Extraction, Features extraction, GLCM, High spatial resolution, Image segmentation, Land cover classification, NDVI, Overall accuracies, Process control, Sentinel 2, supervised classification, Textures @conference{Chairet2019497,Since 2015, the Sentinel 2 satellite provides a medium to high spatial resolution (10m-30m) images. For studying the land cover of Gabes area, located in the South- East of Tunisia, we exploited the 10 m bands of this satellite. We have tested the supervised classification with the SVM classifier. The classification is preceded by a segmentation step. The spectral data, the vegetation index and the texture metrics (GLCM) are used for training. The best Overall Accuracy OA (92, 12%) is obtained when all the used features are combined. © 2019 IEEE. |
Publications
2021 |
3D Textures Analysis Based on Features Extraction Article de journal Dans: Smart Sensors, Measurement and Instrumentation, vol. 40, p. 231-256, 2021, ISSN: 21948402, (cited By 1). |
2019 |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |
Features extraction and land cover classification using Sentinel 2 data Conférence 2019, (Cited by: 6). |