Letteratura scientifica selezionata sul tema "Images texturées"
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Articoli di riviste sul tema "Images texturées":
Girod, Luc, e Marc Pierrot-Deseilligny. "L'Égalisation radiométrique de nuages de points 3D issus de corrélation dense". Revue Française de Photogrammétrie et de Télédétection, n. 206 (19 giugno 2014): 3–14. http://dx.doi.org/10.52638/rfpt.2014.90.
Boussidi, Brahim, Ronan Fablet, Emmanuelle Autret e Bertrand Chapron. "Accroissement stochastique de la résolution spatiale des traceurs géophysiques de l'océan: application aux observations satellitaires de la température de surface de l'océan". Revue Française de Photogrammétrie et de Télédétection, n. 202 (16 aprile 2014): 66–78. http://dx.doi.org/10.52638/rfpt.2013.52.
Hemalatha, S., e S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection". International Journal of Ambient Computing and Intelligence 7, n. 2 (luglio 2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.
Bhaumik, Shubrajit, Viorel Paleu, Dhrubajyoti Chowdhury, Adarsh Batham, Udit Sehgal, Basudev Bhattacharya, Chiradeep Ghosh e Shubhabrata Datta. "Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions". Materials 15, n. 23 (27 novembre 2022): 8445. http://dx.doi.org/10.3390/ma15238445.
Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias e Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras". Sensors 21, n. 9 (7 maggio 2021): 3248. http://dx.doi.org/10.3390/s21093248.
Wen, Mingyun, Jisun Park e Kyungeun Cho. "Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks". Remote Sensing 13, n. 21 (22 ottobre 2021): 4254. http://dx.doi.org/10.3390/rs13214254.
Volkova, Natalya P., e Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS". Herald of Advanced Information Technology 3, n. 4 (20 novembre 2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.
Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli e Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification". Sensors 20, n. 18 (22 settembre 2020): 5432. http://dx.doi.org/10.3390/s20185432.
Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri e M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images". International Journal of Electrical and Computer Engineering (IJECE) 6, n. 3 (1 giugno 2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.9233.
Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri e M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images". International Journal of Electrical and Computer Engineering (IJECE) 6, n. 3 (1 giugno 2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.pp1152-1160.
Tesi sul tema "Images texturées":
Konik, Hubert. "Contribution de l'approche pyramidale à la segmentation des images texturées". Saint-Etienne, 1994. http://www.theses.fr/1994STET4018.
Germain, Christian. "Contribution à la caractérisation multi-échelle de l'anisotropie des images texturées". Phd thesis, Université Sciences et Technologies - Bordeaux I, 1997. http://tel.archives-ouvertes.fr/tel-00166497.
Le premier chapitre définit la notion de texture et celle d'échelle d'observation. Les différentes approches de caractérisation texturale existantes sont présentées et leur aptitude à rendre compte des phénomènes directionnels à différentes échelles d'observation est évaluée.
Le second chapitre présente les méthodes les plus courantes pour l'estimation de l'orientation dominante d'une texture. Un indicateur local est ensuite proposé : le Vecteur Directionnel Moyen. Il s'appuie sur des caractéristiques locales et peut être calculé à toute échelle d'observation. Ses performances sont étudiées sur des images de synthèse et sur des textures naturelles.
Le troisième chapitre introduit un nouvel indicateur d'anisotropie nommé Iso. Il est basé sur le calcul des différences locales des Vecteurs Directionnels Moyens obtenus à une échelle donnée. Ses performances sont comparées à celles des estimateurs classiques de dispersion directionnelle.
Le dernier chapitre est consacré à l'évaluation de l'anisotropie de textures complexes (microscopiques et macroscopiques) en fonction de l'échelle d'observation. Un modèle de texture complexe est construit et le comportement de l'indicateur Iso sur ce modèle est établi. L'indicateur est ensuite appliqué à la caractérisation de textures naturelles et de synthèse. Il est ensuite montré que l'évolution de cet indicateur en fonction de l'échelle d'observation fournit une courbe qui caractérise à la fois l'anisotropie de la texture traitée ainsi que la taille des différentes primitives texturales microscopiques et macroscopiques formant cette texture. L'indicateur Iso , calculé à différentes échelles, est appliqué à des textures synthétiques, à des textures de l'album de Brodatz ainsi qu'à des images de matériaux composites observés par microscopie électronique à transmission.
Faucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes". Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.
In this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images
Yum-Oh, Suk. "Utilisation de l'information de phase en segmentation et classification des images texturées". La Rochelle, 1995. http://www.theses.fr/1995LAROS003.
Formont, Pierre. "Outils statistiques et géométriques pour la classification des images SAR polarimétriques hautement texturées". Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00983304.
Formont, P. "Outils statistiques et géométriques pour la classification des images SAR polarimétriques hautement texturées". Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-01020050.
Joseph, Pierre. "Etude expérimentale du glissement sur surfaces lisses et texturées". Paris 6, 2005. http://www.theses.fr/2005PA066214.
Paulhac, Ludovic. "Outils et méthodes d'analyse d'images 3D texturées : application à la segmentation des images échographiques". Phd thesis, Université François Rabelais - Tours, 2009. http://tel.archives-ouvertes.fr/tel-00576507.
Nguyen, Tien Sy. "Extraction de structures fines sur des images texturées : application à la détection automatique de fissures sur des images de surface de chaussées". Phd thesis, Université d'Orléans, 2010. http://tel.archives-ouvertes.fr/tel-00592482.
Karoui, Imen. "Segmentation par méthodes markoviennes et variationnelles des images texturées : application à la caractérisation sonar des fonds marins". Télécom Bretagne, 2007. http://www.theses.fr/2007TELB0035.
This work is concerned with the characterization and the segmentation of high resolution sonar images. We are interested in the texture information within these images. The report is divided into three parts. The two former parts are concerned with natural texture analysis in general. The third one presents an application to sonar image segmentation. In the first part, we describe texture by a set of empirical distribution estimated on texture responses to a set of different filters computed for different parameterizations. We fuse the contribution of the different features using a weighting scheme: we define a new similarity measure between textures, as a weighted sum of Kullback-Leibler divergence between texture features. The weights are estimated according to global margin maximization criterion. According to weight values, we select the most discriminating features. In the second part, we exploit this similarity measure to develop supervised and unsupervised segmentation algorithms. We propose two segmentation methods: one "pixel-based" method formulated in a bayesian based Markov Random Field (MRF) framework and a variational "region-based" approach implemented with the level set technique. In the third part, we present an application to the characterization and the segmentation of sonar images. We show sidescan sonar feature dependency with incidence angles and we describe the modification of our similarity measure and our segmentation algorithms to take into account the angular feature dependency
Libri sul tema "Images texturées":
Hung, Chih-Cheng, Enmin Song e Yihua Lan. Image Texture Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1.
Stoichita, Victor. Les Fileuses de Velazquez: Textes, textures, images. Paris]: Fayard, 2018.
Gimel’farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4461-2.
Gimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999.
Gimelʹfarb, Georgiĭ Lʹvovich. Image textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.
Harris, David Earl. Texture analysis of skin cancer images. Ann Arbor, Mich: UMI, 1991.
Korn, Christopher A. Markov random field textures and applications in image processing. Monterey, Calif: Naval Postgraduate School, 1997.
Wood, E. J. Carpet texture measurement using image analysis. Christchurch: Wronz, 1987.
Spann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.
Chaki, Jyotismita, e Nilanjan Dey. Texture Feature Extraction Techniques for Image Recognition. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0853-0.
Capitoli di libri sul tema "Images texturées":
Xie, Xianghua, e Majid Mirmehdi. "Texture Exemplars for Defect Detection on Random Textures". In Pattern Recognition and Image Analysis, 404–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_46.
Magnenat-Thalmann, Nadia, e Daniel Thalmann. "Texture". In Image Synthesis, 221–46. Tokyo: Springer Japan, 1987. http://dx.doi.org/10.1007/978-4-431-68060-4_12.
Peters, James F. "Texture and Texture Set Patterns". In Topology of Digital Images, 301–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53845-2_11.
Tomita, Fumiaki, e Saburo Tsuji. "Image Segmentation". In Computer Analysis of Visual Textures, 37–55. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1553-7_3.
Jähne, Bernd. "Texture". In Digital Image Processing, 185–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-11565-7_9.
Jähne, Bernd. "Texture". In Digital Image Processing, 185–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21817-4_9.
Jähne, Bernd. "Texture". In Digital Image Processing, 413–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_15.
Jähne, Bernd. "Texture". In Digital Image Processing, 383–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03477-4_12.
Jähne, Bernd. "Texture". In Digital Image Processing, 185–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03174-2_9.
Kinser, Jason M. "Texture Recognition". In Image Operators, 221–42. First edition. | Boca Raton, FL: CRC Press/Taylor & Francis Group, [2019] |: CRC Press, 2018. http://dx.doi.org/10.1201/9780429451188-16.
Atti di convegni sul tema "Images texturées":
Sanzharov, Vadim Vladimirovich, e Vladimir Alexandrovich Frolov. "Viewpoint Selection for Texture Reconstruction with Inverse Rendering". In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-66-77.
Kimura, Soichiro, Kensuke Tobitani e Noriko Nagata. "BTF Prediction Model using Unsupervised Learning". In 8th International Conference on Control, Modeling and Computing (CMC 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120505.
Turner, Mark R. "Gabor functions and textural segmentation". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj38.
Aloimonos, John (Yannis), e Paul Chou. "Detection of surface orientation from texture". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.ww1.
Quediman, Barbara, Enrique Estrada, Radompon Sungkorn e Jonas Toelke. "BHI Logs and ML Automated Pre-Salt Carbonate: Texture Recognition and Petrophysical Properties Propagation Using Image Log, Core Images and Porosity-Permeability From Plugs". In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0095.
Shi, Jingang, Yusi Wang, Songlin Dong, Xiaopeng Hong, Zitong Yu, Fei Wang, Changxin Wang e Yihong Gong. "IDPT: Interconnected Dual Pyramid Transformer for Face Super-Resolution". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/182.
Silva, Daniel, e Romuere Silva. "Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12151.
Domash, Lawrence, Vincent Ryan e Parviz Tayebati. "Optical processing of fractal images". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thqq3.
Turner, Mark R. "Local spectral analysis of texture gradients". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tuu28.
Yang, S., Y. Wang e C. Shrivastava. "Sedimentary Analysis Via Automatic Image Segmentation and Clustering with the LWD OBM Resistivity Image: A Case Study from Gulf of Mexico". In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214908-ms.
Rapporti di organizzazioni sul tema "Images texturées":
Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee e Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, dicembre 2023. http://dx.doi.org/10.36501/0197-9191/23-029.
McKay, Paul, e C. A. Blain. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2013. http://dx.doi.org/10.21236/ada609737.
LaCascia, Marco, John Isidoro e Stan Sclaroff. Head Tracking via Robust Registration in Texture Map Images. Fort Belvoir, VA: Defense Technical Information Center, agosto 1998. http://dx.doi.org/10.21236/ada366993.
Carasso, Alfred S. Singular integrals, image smoothness, and the recovery of texture in image deblurring. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.7005.
Wendelberger, James G. Localized Similar Image Texture in Images of Sample Laser Confocal Microscope for Area: FY15 DE07 SW C1 Zone 1 & 2 Section b. Office of Scientific and Technical Information (OSTI), febbraio 2019. http://dx.doi.org/10.2172/1496724.
Gletsos, M., S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita e D. Kelekis. Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, ottobre 2001. http://dx.doi.org/10.21236/ada412422.
Pe-Piper, G., D. J W Piper, J. Nagle e P. Opra. Petrography of bedrock and ice-rafted granules: Flemish Cap, offshore Newfoundland and Labrador. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331224.
Du, Li-Jen. Segmentation of Synthetic Aperture Radar (SAR) Images of Ocean Surface by the Texture Energy Transform Method. Fort Belvoir, VA: Defense Technical Information Center, agosto 1988. http://dx.doi.org/10.21236/ada199536.
Andrawes, Bassem, Ernesto Perez Claros e Zige Zhang. Bond Characteristics and Experimental Behavior of Textured Epoxy-coated Rebars Used in Concrete Bridge Decks. Illinois Center for Transportation, gennaio 2022. http://dx.doi.org/10.36501/0197-9191/22-001.
King, E. L., A. Normandeau, T. Carson, P. Fraser, C. Staniforth, A. Limoges, B. MacDonald, F. J. Murrillo-Perez e N. Van Nieuwenhove. Pockmarks, a paleo fluid efflux event, glacial meltwater channels, sponge colonies, and trawling impacts in Emerald Basin, Scotian Shelf: autonomous underwater vehicle surveys, William Kennedy 2022011 cruise report. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331174.