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Artykuły w czasopismach na temat "Imagerie hyperspectrale – Analyse informatique"
Spiller, D., L. Ansalone, S. Amici, A. Piscini i P. P. Mathieu. "ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (28.06.2021): 215–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-215-2021.
Pełny tekst źródłaNyandue Ompola, José. "La cartographie numérique et son apport dans l’organisation du recensement en République Démocratique du Congo". Revue Congolaise des Sciences & Technologies 01, nr 02 (20.11.2022): 110–18. http://dx.doi.org/10.59228/rcst.022.v1.i2.14.
Pełny tekst źródłaRozprawy doktorskie na temat "Imagerie hyperspectrale – Analyse informatique"
Pelletier, Bruno. "Traitement neuronal de l'information hyperspectrale". Toulouse, ENSAE, 2002. http://www.theses.fr/2002ESAE0014.
Pełny tekst źródłaLefévre, Soizic. "Caractérisation de la qualité des raisins par imagerie". Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS017.
Pełny tekst źródłaIdentifying the health conditions of the grapes at harvest time is a major issue in order to produce quality wines. To meet this issue, data are acquired by spectrometry, hyperspectral imaging and RGB imaging on grape samples during harvest.Several pre-treatments adapted to each type of data are applied such as normalization, reduction, extraction of characteristic vectors, and segmentation of useful areas. From an imaging point of view, the reconstruction in false colors of hyperspectral images, far from reality, doesn’t allow to label all the intra-class diversity. On the other hand, the visual quality of RGB imaging enables accurate class labelling. From this labelling, classifiers such as support vector machines, random forests, maximum likelihood estimation, spectral mapping, k-means are tested and trained on labelled bases. Depending on the nature of the data, the most effective is applied to whole images of grape clusters or crates of grapes of several grape varieties from different parcels.The quality indices obtained from RGB image processing are very close to the estimates made by experts in the field
Faivre, Adrien. "Analyse d'image hyperspectrale". Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCD075/document.
Pełny tekst źródłaThis dissertation addresses hyperspectral image analysis, a set of techniques enabling exploitation of micro-spectroscopy images. Images produced by these sensors constitute cubic arrays, meaning that every pixel in the image is actually a spectrum.The size of these images, which is often quite large, calls for an upgrade for classical image analysis algorithms.We start out our investigation with clustering techniques. The main idea is to regroup every spectrum contained in a hyperspectralimage into homogeneous clusters. Spectrums taken across the image can indeed be generated by similar materials, and hence display spectral signatures resembling each other. Clustering is a commonly used method in data analysis. It belongs nonetheless to a class of particularly hard problems to solve, named NP-hard problems. The efficiency of a few heuristics used in practicewere poorly understood until recently. We give theoretical arguments guaranteeing success when the groups studied displaysome statistical property.We then study unmixing techniques. The objective is no longer to decide to which class a pixel belongs, but to understandeach pixel as a mix of basic signatures supposed to arise from pure materials. The mathematical underlying problem is again NP-hard.After studying its complexity, and suggesting two lengthy relaxations, we describe a more practical way to constrain the problemas to obtain regularized solutions.We finally give an overview of other hyperspectral image analysis methods encountered during this thesis, amongst whomare independent component analysis, non-linear dimension reduction, and regression against a spectrum library
Fasquelle, François. "L'identification d'explosifs par imagerie hyperspectrale : spectromètre par transformation de Fourier aéroporté". Thesis, Université Laval, 2006. http://www.theses.ulaval.ca/2006/24097/24097.pdf.
Pełny tekst źródłaKhoder, Jihan. "Nouvel Algorithme pour la Réduction de la Dimensionnalité en Imagerie Hyperspectrale". Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2013. http://tel.archives-ouvertes.fr/tel-00939018.
Pełny tekst źródłaNoyel, Guillaume. "Filtrage, réduction de dimension, classification et segmentation morphologique hyperspectrale". Phd thesis, École Nationale Supérieure des Mines de Paris, 2008. http://pastel.archives-ouvertes.fr/pastel-00004473.
Pełny tekst źródłaDelcourt, Jonathan. "Un système intégré d'acquisition 3D multispectral : acquisition, codage et compression des données". Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00578448.
Pełny tekst źródłaVohl, Dany. "Algorithmes de compression d'images hyperspectrales astrophysiques". Thesis, Université Laval, 2013. http://www.theses.ulaval.ca/2013/30110/30110.pdf.
Pełny tekst źródłaSpIOMM, the Imaging Fourier Transform Spectrometer of the Observatoire du Mont-Mégantic generates huge files of about 700 MB per file on average, and SITELLE, its successor will generate files of a few GB. Since several files can be generated during an observation night and the astronomers are not always on-site, there is an increasing need for both storage and transmission. To minimize storage space, bandwidth use and transmission time, three data compression techniques are presented. The first two techniques are lossless data compression and the third one is lossy. The lossless techniques give better results than generic techniques that are zip and gzip2, with compression ratios varying from 1:19 : 1 to 1:22 : 1. The lossy technique compresses files up to a 64 : 1 ratio. The effect of the lossy process on the photometric measurements and the spectra analysis is also studied.
Lagrange, Adrien. "From representation learning to thematic classification - Application to hierarchical analysis of hyperspectral images". Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0095.
Pełny tekst źródłaNumerous frameworks have been developed in order to analyze the increasing amount of available image data. Among those methods, supervised classification has received considerable attention leading to the development of state-of-the-art classification methods. These methods aim at inferring the class of each observation given a specific class nomenclature by exploiting a set of labeled observations. Thanks to extensive research efforts of the community, classification methods have become very efficient. Nevertheless, the results of a classification remains a highlevel interpretation of the scene since it only gives a single class to summarize all information in a given pixel. Contrary to classification methods, representation learning methods are model-based approaches designed especially to handle high-dimensional data and extract meaningful latent variables. By using physic-based models, these methods allow the user to extract very meaningful variables and get a very detailed interpretation of the considered image. The main objective of this thesis is to develop a unified framework for classification and representation learning. These two methods provide complementary approaches allowing to address the problem using a hierarchical modeling approach. The representation learning approach is used to build a low-level model of the data whereas classification is used to incorporate supervised information and may be seen as a high-level interpretation of the data. Two different paradigms, namely Bayesian models and optimization approaches, are explored to set up this hierarchical model. The proposed models are then tested in the specific context of hyperspectral imaging where the representation learning task is specified as a spectral unmixing problem
Méteau, Jérémy. "Instrumentation optique pour la caractérisation des tissus : analyse de la complémentarité et des limites techniques de fluorescence hyperspectrale et de Tomographie Optique Cohérente en vue de leur intégration multimodale". Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2041/document.
Pełny tekst źródłaThe aim of this activity is the development of a mono point imaging fiber system which uses hyperspectral multi-excitation auto fluorescence technique for biological tissues and the study of an Optical Coherence Tomography system like another modality. At first, this report presents the optical properties of biological tissues and the relevant fluorophores for cancerous tumors detection. Secondly, the fluorescence imaging system instrumentation and hyperspectral analysis are presented with in vitro results. The third part presents the "scan free" optical coherence tomography system which is able to image without optical displacement. It's characterized and have interesting functionality like depth dependant dispersion compensation. These both techniques are complementary because they get different kind of information. The information of the first one is about biochemical composition of the tissues and the information of the second one is about the stucture
Książki na temat "Imagerie hyperspectrale – Analyse informatique"
Capture, Canada Image Generation and. Report of Working Group 2, Medical Imaging Technology Roadmap. Ottawa, Ont: Industry Canada, 2001.
Znajdź pełny tekst źródłaCanada, Canada Industry, red. Image generation and capture: Report of working group 2 : medical imaging technology roadmap. Ottawa: Industry Canada, 2001.
Znajdź pełny tekst źródłaBerry, Elizabeth. A practical approach to medical image processing. New York: Taylor & Francis, 2008.
Znajdź pełny tekst źródłaXiong, Wei, Rodrigo Rojas Moraleda, Nektarios A. Valous i Niels Halama. Computational Topology for Biomedical Image and Data Analysis: Theory and Applications. Taylor & Francis Group, 2019.
Znajdź pełny tekst źródłaXiong, Wei, Rodrigo Rojas Moraleda, Niels Halama i Nektarios Valous. Computational Topology for Biomedical Image and Data Analysis. Taylor & Francis Group, 2021.
Znajdź pełny tekst źródłaComputational Topology for Biomedical Image and Data Analysis: Theory and Applications. Taylor & Francis Group, 2019.
Znajdź pełny tekst źródłaXiong, Wei, Rodrigo Rojas Moraleda, Nektarios A. Valous i Niels Halama. Computational Topology for Biomedical Image and Data Analysis: Theory and Applications. Taylor & Francis Group, 2019.
Znajdź pełny tekst źródłaSonka, Milan, i Gary E. Christensen. Information Processing in Medical Imaging: 19th International Conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005, Proceedings. Springer London, Limited, 2005.
Znajdź pełny tekst źródła(Editor), Gary E. Christensen, i Milan Sonka (Editor), red. Information Processing in Medical Imaging: 19th International Conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005, Proceedings (Lecture Notes in Computer Science). Springer, 2005.
Znajdź pełny tekst źródłaHemanth, D. Jude, Utku Kose i Omer Deperlioglu. Deep Learning for Biomedical Applications. Taylor & Francis Group, 2021.
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