03892 am 2200853 n 450 9910568193303321202110072-84016-423-X10.4000/books.pupo.21622(CKB)4100000012875600(FrMaCLE)OB-pupo-21622(oapen)https://directory.doabooks.org/handle/20.500.12854/86540(PPN)263271609(EXLCZ)99410000001287560020220520j|||||||| ||| 0freuu||||||m||||txtrdacontentcrdamediacrrdacarrierMuséoscopies Fictions du musée au cinéma /Joséphine Jibokji, Barbara Le Maître, Natacha Pernac, Jennifer VerraesNanterre Presses universitaires de Paris Nanterre20211 online resource (358 p.) L’œil du cinéma2-84016-281-4 Architectures grandioses, expositions médiatisées à outrance et instituées en rituels saisonniers, le musée est aujourd’hui investi d’une attractivité touristique et d’une charge patrimoniale, politique, symbolique sans précédent. Ce qui s’y monnaye est-il cette « monnaie de l’absolu » dont André Malraux célébra l’universalité ? L’interrogation court tout au long de cet ouvrage qui choisit le prisme du cinéma de fiction pour revisiter le musée, dans ses missions et mythologies traditionnelles mais aussi dans ses coulisses et sa violence. Au final, les intrigues muséales tramées entre autres par Michael Curtiz, Tsai Ming-liang, Jean- Luc Godard, les frères Quay, Sanjay Gadhvi, Marco Bellocchio ou Charles Crichton sondent notre rapport fétichiste à l’œuvre d’art et notre regard sur le patrimoine. À travers des analyses subtiles et décapantes, muséologues, historiens de l’art et du cinéma nouent un dialogue qui atteste la puissance discursive de la fiction. Il en naît aussi une éclatante relance théorique sur les fonctions du musée, sur les valeurs qui s’y transmettent, s’y échangent, s’y révisent et s’y réinventent.MusÃoscopies ArtFilm Radio Televisionarchitecturehistoire de l’artcinémamuséographiearchitecturehistoire de l’artcinémamuséographieArtFilm Radio Televisionarchitecturehistoire de l’artcinémamuséographieAckerman Ada1287569Bourget Jean-Loup572752Cheval Olivier1287570Faucon Térésa1287571Gailleurd Céline1287572Gauthier Michel113758Giannouri Evgenia1287573Grignard Eline1287574Herard Ivan1287575Jacobs Steven970007Jibokji Joséphine1287576Lavin Mathias1222787Le Maître Barbara1287577Liandrat-Guigues Suzanne561210Maillet Arnaud1285397Mairesse François1287578Pernac Natacha1287579Savoy Bénédicte1088726Schifano Laurence1287580Trias Jean-Philippe1287581Verraes Jennifer1287582Zvonkine Eugénie1287583Jibokji Joséphine1287576Le Maître Barbara772788Pernac Natacha1287579Verraes Jennifer1287582FR-FrMaCLEBOOK9910568193303321Muséoscopies3020192UNINA04871nam 2201261z- 450 991063998470332120240301170435.03-0365-6084-X(CKB)5470000001633507(oapen)https://directory.doabooks.org/handle/20.500.12854/95818(EXLCZ)99547000000163350720202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierArtificial Intelligence-Based Learning Approaches for Remote SensingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (382 p.)3-0365-6083-1 The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.Technology: general issuesbicsscHistory of engineering & technologybicsscEnvironmental science, engineering & technologybicsscpine wilt disease datasetGIS application visualizationtest-time augmentationobject detectionhard negative miningvideo synthetic aperture radar (SAR)moving targetshadow detectiondeep learningfalse alarmsmissed detectionssynthetic aperture radar (SAR)on-boardship detectionYOLOv5lightweight detectorremote sensing imagespectral domain translationgenerative adversarial networkpaired translationsynthetic aperture radarship instance segmentationglobal context modelingboundary-aware box predictionland-use and land-coverbuilt-up expansionprobability modellinglandscape fragmentationmachine learningsupport vector machinefrequency ratiofuzzy logicartificial intelligenceremote sensinginterferometric phase filteringsparse regularization (SR)deep learning (DL)neural convolutional network (CNN)semantic segmentationopen databuilding extractionunetdeeplabclassifying-inversion methodAISatmospheric ductship detection and classificationrotated bounding boxattentionfeature alignmentweather nowcastingResNeXtradar dataspectral-spatial interaction networkspectral-spatial attentionpansharpeningUAV visual navigationSiamese networkmulti-order featureMIoUimbalanced data classificationdata over-samplinggraph convolutional networksemi-supervised learningtroposcattertropospheric turbulenceintercity co-channel interferenceconcrete bridgevisual inspectiondefectdeep convolutional neural networktransfer learninginterpretation techniquesweakly supervised semantic segmentationTechnology: general issuesHistory of engineering & technologyEnvironmental science, engineering & technologyJeon Gwanggiledt1279103Jeon GwanggilothBOOK9910639984703321Artificial Intelligence-Based Learning Approaches for Remote Sensing3014581UNINA