Auswahl der wissenschaftlichen Literatur zum Thema „Textured images“
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Zeitschriftenartikel zum Thema "Textured images"
Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias und Vítor Santos. „Robust Texture Mapping Using RGB-D Cameras“. Sensors 21, Nr. 9 (07.05.2021): 3248. http://dx.doi.org/10.3390/s21093248.
Der volle Inhalt der QuelleHemalatha, S., und 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, Nr. 2 (Juli 2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.
Der volle Inhalt der QuelleDal’Col, Lucas, Daniel Coelho, Tiago Madeira, Paulo Dias und Miguel Oliveira. „A Sequential Color Correction Approach for Texture Mapping of 3D Meshes“. Sensors 23, Nr. 2 (05.01.2023): 607. http://dx.doi.org/10.3390/s23020607.
Der volle Inhalt der QuelleBhaumik, Shubrajit, Viorel Paleu, Dhrubajyoti Chowdhury, Adarsh Batham, Udit Sehgal, Basudev Bhattacharya, Chiradeep Ghosh und Shubhabrata Datta. „Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions“. Materials 15, Nr. 23 (27.11.2022): 8445. http://dx.doi.org/10.3390/ma15238445.
Der volle Inhalt der QuelleCoelho, Daniel, Lucas Dal’Col, Tiago Madeira, Paulo Dias und Miguel Oliveira. „A Robust 3D-Based Color Correction Approach for Texture Mapping Applications“. Sensors 22, Nr. 5 (23.02.2022): 1730. http://dx.doi.org/10.3390/s22051730.
Der volle Inhalt der QuelleAkl, Adib. „Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images“. Cybernetics and Information Technologies 18, Nr. 1 (01.03.2018): 51–68. http://dx.doi.org/10.2478/cait-2018-0005.
Der volle Inhalt der QuelleBeschastnov, Nikolay P., Irina V. Rybaulina und Evdokia N. Dergileva. „FACTURE, TEXTURE AND TEHNO-ORNAMENT IN MODERN DESIGN: FUNCTION AND ARTISTIC MEANING“. Technologies & Quality 51, Nr. 1 (29.04.2021): 40–45. http://dx.doi.org/10.34216/2587-6147-2021-1-51-40-45.
Der volle Inhalt der QuelleBarburiceanu, Stefania, Romulus Terebes und Serban Meza. „3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors“. Applied Sciences 11, Nr. 5 (05.03.2021): 2332. http://dx.doi.org/10.3390/app11052332.
Der volle Inhalt der QuelleWen, Mingyun, Jisun Park und Kyungeun Cho. „Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks“. Remote Sensing 13, Nr. 21 (22.10.2021): 4254. http://dx.doi.org/10.3390/rs13214254.
Der volle Inhalt der QuelleGimel'Farb, Georgy L., und Anil K. Jain. „On retrieving textured images from an image database“. Pattern Recognition 29, Nr. 9 (September 1996): 1461–83. http://dx.doi.org/10.1016/0031-3203(96)00011-8.
Der volle Inhalt der QuelleDissertationen zum Thema "Textured images"
Li, Zhongqiang. „Segmentation of textured images“. Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.
Der volle Inhalt der QuelleLeng, Xiaoling. „Analysis of some textured images by transputer“. Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324405.
Der volle Inhalt der QuelleNoriega, Leonardo Antonio. „The colorimetric segmentation of textured digital images“. Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.
Der volle Inhalt der QuelleBradbury, Teresa Ann. „Textured imprints, images, social change, and cultural memory“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29144.pdf.
Der volle Inhalt der QuelleWilliams, Ian Anthony. „Edge detection of textured images using multiple scales and statistics“. Thesis, Manchester Metropolitan University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491176.
Der volle Inhalt der QuelleVăcar, Cornelia Paula. „Inversion for textured images : unsupervised myopic deconvolution, model selection, deconvolution-segmentation“. Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0131/document.
Der volle Inhalt der QuelleThis thesis is addressing a series of inverse problems of major importance in the fieldof image processing (image segmentation, model choice, parameter estimation, deconvolution)in the context of textured images. In all of the aforementioned problems theobservations are indirect, i.e., the textured images are affected by a blur and by noise. Thecontributions of this work belong to three main classes: modeling, methodological andalgorithmic. From the modeling standpoint, the contribution consists in the development of a newnon-Gaussian model for textures. The Fourier coefficients of the textured images are modeledby a Scale Mixture of Gaussians Random Field. The Power Spectral Density of thetexture has a parametric form, driven by a set of parameters that encode the texture characteristics.The methodological contribution is threefold and consists in solving three image processingproblems that have not been tackled so far in the context of indirect observationsof textured images. All the proposed methods are Bayesian and are based on the exploitingthe information encoded in the a posteriori law. The first method that is proposed is devotedto the myopic deconvolution of a textured image and the estimation of its parameters.The second method achieves joint model selection and model parameters estimation froman indirect observation of a textured image. Finally, the third method addresses the problemof joint deconvolution and segmentation of an image composed of several texturedregions, while estimating at the same time the parameters of each constituent texture.Last, but not least, the algorithmic contribution is represented by the development ofa new efficient version of the Metropolis Hastings algorithm, with a directional componentof the proposal function based on the”Newton direction” and the Fisher informationmatrix. This particular directional component allows for an efficient exploration of theparameter space and, consequently, increases the convergence speed of the algorithm.To summarize, this work presents a series of methods to solve three image processingproblems in the context of blurry and noisy textured images. Moreover, we present twoconnected contributions, one regarding the texture models andone meant to enhance theperformances of the samplers employed for all of the three methods
Meléndez, Rodríguez Jaime Christian. „Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification“. Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/8487.
Der volle Inhalt der QuelleEsta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes.
Dura, Martinez Esther. „Reconstruction and classification of man-made objects and textured seafloors from side-scan sonar images“. Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/409.
Der volle Inhalt der QuelleAchddou, Raphaël. „Synthetic learning for neural image restoration methods“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT006.
Der volle Inhalt der QuellePhotography has become an important part of our lives. In addition, expectations in terms of image quality are increasing while the size of imaging devices is decreasing. In this context, the improvement of image processing algorithms is essential.In this manuscript, we are particularly interested in image restoration tasks. The goal is to produce a clean image from one or more noisy observations of the same scene. For these problems, deep learning methods have grown dramatically in the last decade, outperforming the state of the art for the vast majority of traditional tests.While these methods produce impressive results, they have a number of drawbacks. First of all, they are difficult to interpret because of their "black box" operation. Moreover, they generalize rather poorly to acquisition or distortion modalities absent from the training database. Finally, they require large databases, which are sometimes difficult to acquire.We propose to attack these different problems by replacing the data acquisition by a simple image generation algorithm, based on the dead leaves model. Although this model is very simple, the generated images have statistical properties close to those of natural images and many invariance properties (scale, translation, rotation, contrast...). Training a restoration network with this kind of image allows us to identify the important properties of the images for the success of the restoration networks. Moreover, this method allows us to get rid of the data acquisition, which can be tedious.After presenting this model, we show that the proposed method allows to obtain restoration performances very close to traditional methods for relatively simple tasks. After some adaptations of the model, synthetic learning also allows us to tackle difficult concrete problems, such as RAW image denoising. We then propose a statistical study of the color distribution of natural images, allowing to elaborate a realistic parametric model of color sampling for our generation algorithm. Finally, we present a new perceptual loss function based on camera evaluation protocols, using the dead leaf images. The training performed with this function shows that we can jointly optimize the evaluation of the cameras, while keeping identical performances on natural images
Casaca, Wallace Correa de Oliveira [UNESP]. „Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais“. Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/94247.
Der volle Inhalt der QuelleFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são designadas para recompor partes perdias ou remover objetos de uma imagem real a partir de formulações envolvendo decomposiçãode imagens e inpainting por exem- plar, enquanto que as duas últimas são empregadas para remover ruído, cujas formulações são baseadas em decomposição de três termos e equações diferenciais parciais não lineares. Resultados experimentais atestam a boa performace dos protótipos apresentados quando comparados à modelagens correlatas da literatura.
In this paper we propose four new approaches to address the problem of restoration of real images containing textures from the perspective of reconstruction of damaged areas, object removal, and denoising topics. The first two approaches are designed to reconstruct missing parts or to remove objects of a real image using formulations based on image de composition and exemplar based inpainting, while the last two other approaches are used to remove noise, whose formulations are based on decomposition of three terms and non- linear partial di®erential equations. Experimental results attest to the good performance of the presented prototypes when compared to modeling related in literature.
Bücher zum Thema "Textured images"
Hung, Chih-Cheng, Enmin Song und Yihua Lan. Image Texture Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13773-1.
Der volle Inhalt der QuelleHarris, David Earl. Texture analysis of skin cancer images. Ann Arbor, Mich: UMI, 1991.
Den vollen Inhalt der Quelle findenLes Fileuses de Velazquez: Textes, textures, images. Paris]: Fayard, 2018.
Den vollen Inhalt der Quelle findenGimel’farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4461-2.
Der volle Inhalt der QuelleGimel'farb, Georgy L. Image Textures and Gibbs Random Fields. Dordrecht: Springer Netherlands, 1999.
Den vollen Inhalt der Quelle findenImage textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.
Den vollen Inhalt der Quelle findenWood, E. J. Carpet texture measurement using image analysis. Christchurch: Wronz, 1987.
Den vollen Inhalt der Quelle findenSpann, Michael. Texture description and segmentation in image processing. Birmingham: University of Aston. Department of Electrical and Electronic Engineering, 1985.
Den vollen Inhalt der Quelle findenChaki, Jyotismita, und Nilanjan Dey. Texture Feature Extraction Techniques for Image Recognition. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0853-0.
Der volle Inhalt der QuelleRand, Robert S. Texture analysis and cartographic feature extraction. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1985.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Textured images"
Rouquet, Catherine, und Pierre Bonton. „Region-based segmentation of textured images“. In Image Analysis and Processing, 11–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_230.
Der volle Inhalt der QuelleMičušík, Branislav, und Allan Hanbury. „Steerable Semi-automatic Segmentation of Textured Images“. In Image Analysis, 35–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_5.
Der volle Inhalt der QuelleSantos, Roi, Xosé R. Fdez-Vidal und Xosé M. Pardo. „Adaptive Line Matching for Low-Textured Images“. In Pattern Recognition and Image Analysis, 192–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_22.
Der volle Inhalt der QuelleCasadei, Stefano, Sanjoy Mitter und Pietro Perona. „Boundary detection in piecewise homogeneous textured images“. In Computer Vision — ECCV'92, 174–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_20.
Der volle Inhalt der QuelleKjell, Bradley P., und Charles R. Dyer. „Segmentation of Textured Images by Pyramid Linking“. In Pyramidal Systems for Computer Vision, 273–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82940-6_17.
Der volle Inhalt der QuelleManfredi, Guido, Michel Devy und Daniel Sidobre. „Textured Object Recognition: Balancing Model Robustness and Complexity“. In Computer Analysis of Images and Patterns, 52–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_5.
Der volle Inhalt der QuelleGrau, Antoni, und Jordi Saludes. „Improved textured images segmentation using an energy functional“. In Image Analysis and Processing, 70–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_186.
Der volle Inhalt der QuelleAzencott, R., C. Graffrigne und C. Labourdette. „Edge Detection and Segmentation of Textured Plane Images“. In Stochastic Models, Statistical Methods, and Algorithms in Image Analysis, 75–88. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9_4.
Der volle Inhalt der QuelleTaha, Bilal, Munawar Hayat, Stefano Berretti und Naoufel Werghi. „Fused Geometry Augmented Images for Analyzing Textured Mesh“. In Lecture Notes in Computer Science, 3–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54407-2_1.
Der volle Inhalt der QuelleLee, Yun-Seok, Seung-Hun Yoo und Chang-Sung Jeong. „Modified Hough Transform for Images Containing Many Textured Regions“. In Rough Sets and Current Trends in Computing, 824–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908029_85.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Textured images"
Sanzharov, Vadim Vladimirovich, und 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.
Der volle Inhalt der QuelleTurner, 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.
Der volle Inhalt der QuelleWeber, Allan G., und Alexander A. Sawchuk. „Segmentation of Textured Images“. In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fb1.
Der volle Inhalt der QuelleWU, DONG-SHENG, LE-NAN WU und BO HUANG. „AUTOMATION TEXTURED AND NON-TEXTURED IMAGES CLASSIFICATION AND RETRIEVAL“. In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704313_0050.
Der volle Inhalt der Quelle„COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES“. In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002042502580263.
Der volle Inhalt der QuelleZeng, Yu, und Biyu Wan. „Saliency Detection in Textured Images“. In 2020 15th International Conference on Computer Science & Education (ICCSE). IEEE, 2020. http://dx.doi.org/10.1109/iccse49874.2020.9201616.
Der volle Inhalt der QuelleHe, Qiang, und Chee-Hung Henry Chu. „Shadow removal from textured images“. In SPIE Defense, Security, and Sensing, herausgegeben von Daniel J. Henry. SPIE, 2009. http://dx.doi.org/10.1117/12.818983.
Der volle Inhalt der QuelleGiovannelli, Jean-Francois, und Cornelia Vacar. „Deconvolution-segmentation for textured images“. In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081195.
Der volle Inhalt der QuelleDolez, Benoit, und Nicole Vincent. „Sample Selection in Textured Images“. In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379132.
Der volle Inhalt der QuelleTurtinen, M., und M. Pietikainen. „Contextual Analysis of Textured Scene Images“. In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.87.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Textured images"
Alhasan, Ahmad, Brian Moon, Doug Steele, Hyung Lee und Abu Sufian. Chip Seal Quality Assurance Using Percent Embedment. Illinois Center for Transportation, Dezember 2023. http://dx.doi.org/10.36501/0197-9191/23-029.
Der volle Inhalt der QuelleMcKay, Paul, und C. A. Blain. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture. Fort Belvoir, VA: Defense Technical Information Center, Januar 2013. http://dx.doi.org/10.21236/ada609737.
Der volle Inhalt der QuelleLaCascia, Marco, John Isidoro und Stan Sclaroff. Head Tracking via Robust Registration in Texture Map Images. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada366993.
Der volle Inhalt der QuelleCarasso, 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.
Der volle Inhalt der QuelleWendelberger, 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), Februar 2019. http://dx.doi.org/10.2172/1496724.
Der volle Inhalt der QuelleGletsos, M., S. G. Mougiakakou, G. K. Matsopoulos, K. S. Nikita und D. Kelekis. Classification of Hepatic Lesions From CT Images Using Texture Features and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2001. http://dx.doi.org/10.21236/ada412422.
Der volle Inhalt der QuelleRosenblum, W. I., C. Salvaggio und J. R. Schott. Selection of optimal textural features for maximum likelihood image classification. Office of Scientific and Technical Information (OSTI), Januar 1990. http://dx.doi.org/10.2172/5098367.
Der volle Inhalt der QuelleDu, 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, August 1988. http://dx.doi.org/10.21236/ada199536.
Der volle Inhalt der QuellePe-Piper, G., D. J W Piper, J. Nagle und 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.
Der volle Inhalt der QuelleAndrawes, Bassem, Ernesto Perez Claros und Zige Zhang. Bond Characteristics and Experimental Behavior of Textured Epoxy-coated Rebars Used in Concrete Bridge Decks. Illinois Center for Transportation, Januar 2022. http://dx.doi.org/10.36501/0197-9191/22-001.
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