Literatura científica selecionada sobre o tema "Images texturées"
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Artigos de revistas sobre o assunto "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.º 206 (19 de junho de 2014): 3–14. http://dx.doi.org/10.52638/rfpt.2014.90.
Texto completo da fonteBoussidi, 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.º 202 (16 de abril de 2014): 66–78. http://dx.doi.org/10.52638/rfpt.2013.52.
Texto completo da fonteHemalatha, 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.º 2 (julho de 2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.
Texto completo da fonteBhaumik, 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.º 23 (27 de novembro de 2022): 8445. http://dx.doi.org/10.3390/ma15238445.
Texto completo da fonteOliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias e Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras". Sensors 21, n.º 9 (7 de maio de 2021): 3248. http://dx.doi.org/10.3390/s21093248.
Texto completo da fonteWen, Mingyun, Jisun Park e Kyungeun Cho. "Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks". Remote Sensing 13, n.º 21 (22 de outubro de 2021): 4254. http://dx.doi.org/10.3390/rs13214254.
Texto completo da fonteVolkova, 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.º 4 (20 de novembro de 2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.
Texto completo da fonteSoares, 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.º 18 (22 de setembro de 2020): 5432. http://dx.doi.org/10.3390/s20185432.
Texto completo da fonteVijay 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.º 3 (1 de junho de 2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.9233.
Texto completo da fonteVijay 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.º 3 (1 de junho de 2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.pp1152-1160.
Texto completo da fonteTeses / dissertações sobre o assunto "Images texturées"
Konik, Hubert. "Contribution de l'approche pyramidale à la segmentation des images texturées". Saint-Etienne, 1994. http://www.theses.fr/1994STET4018.
Texto completo da fonteGermain, 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.
Texto completo da fonteLe 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.
Texto completo da fonteIn 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.
Texto completo da fonteFormont, 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.
Texto completo da fonteFormont, 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.
Texto completo da fonteJoseph, Pierre. "Etude expérimentale du glissement sur surfaces lisses et texturées". Paris 6, 2005. http://www.theses.fr/2005PA066214.
Texto completo da fontePaulhac, 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.
Texto completo da fonteNguyen, 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.
Texto completo da fonteKaroui, 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.
Texto completo da fonteThis 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
Livros sobre o assunto "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.
Texto completo da fonteLes Fileuses de Velazquez: Textes, textures, images. Paris]: Fayard, 2018.
Encontre o texto completo da fonteGimel’farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4461-2.
Texto completo da fonteGimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999.
Encontre o texto completo da fonteImage textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.
Encontre o texto completo da fonteHarris, David Earl. Texture analysis of skin cancer images. Ann Arbor, Mich: UMI, 1991.
Encontre o texto completo da fonteKorn, Christopher A. Markov random field textures and applications in image processing. Monterey, Calif: Naval Postgraduate School, 1997.
Encontre o texto completo da fonteWood, E. J. Carpet texture measurement using image analysis. Christchurch: Wronz, 1987.
Encontre o texto completo da fonteSpann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.
Encontre o texto completo da fonteChaki, 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.
Texto completo da fonteCapítulos de livros sobre o assunto "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.
Texto completo da fonteMagnenat-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.
Texto completo da fontePeters, 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.
Texto completo da fonteTomita, 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.
Texto completo da fonteJähne, 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.
Texto completo da fonteJähne, 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.
Texto completo da fonteJähne, 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.
Texto completo da fonteJähne, 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.
Texto completo da fonteJähne, 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.
Texto completo da fonteKinser, 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "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.
Texto completo da fonteKimura, 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.
Texto completo da fonteTurner, 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.
Texto completo da fonteAloimonos, 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.
Texto completo da fonteQuediman, 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.
Texto completo da fonteShi, 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.
Texto completo da fonteSilva, 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.
Texto completo da fonteDomash, 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.
Texto completo da fonteTurner, 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.
Texto completo da fonteYang, 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Images texturées"
Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee e Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, dezembro de 2023. http://dx.doi.org/10.36501/0197-9191/23-029.
Texto completo da fonteMcKay, 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, janeiro de 2013. http://dx.doi.org/10.21236/ada609737.
Texto completo da fonteLaCascia, Marco, John Isidoro e Stan Sclaroff. Head Tracking via Robust Registration in Texture Map Images. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1998. http://dx.doi.org/10.21236/ada366993.
Texto completo da fonteCarasso, 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.
Texto completo da fonteWendelberger, 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), fevereiro de 2019. http://dx.doi.org/10.2172/1496724.
Texto completo da fonteGletsos, 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, outubro de 2001. http://dx.doi.org/10.21236/ada412422.
Texto completo da fontePe-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.
Texto completo da fonteDu, 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 de 1988. http://dx.doi.org/10.21236/ada199536.
Texto completo da fonteAndrawes, 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, janeiro de 2022. http://dx.doi.org/10.36501/0197-9191/22-001.
Texto completo da fonteKing, 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.
Texto completo da fonte