9786207484201 - forgery detection of digital images: forensic science research summary di u, dr.sivaji (6 risultati)
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 57,76
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 56,35
EUR 7,60 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 43,90
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 68 pp. Englisch.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 58,00
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 43,90
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The current study indicates that deep learning may be effectively used in applications including picture categorization, image identification, and object recognition by using several CNN architectures. On altered and/or bigger datasets…, cost-effective picture classification is accomplished, and enhanced image feature mapping is derived from related images in text metadata using CNNs. Given the limited association between feature labels and comparable (and/or unrelated) pictures, employing feature map representations is demonstrated to be cheaper and quicker, but it does not increase the quality of the image classifications, suggesting that this technique is not ideal for assessing quality. However, using the newly acquired learnt weights, the findings of the current study may inspire further research into alternative counterfeit detection methods. Overall, our study shows that metadata sampling and categorization need a highly disciplined scaling model, which can be scored by using a pre-trained model, and which may be further developed in future phases.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 44,59
EUR 60,60 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The current study indicates that deep learning may be effectively used in applications including picture categorization, image identification, and object recognition by using several CNN architectures. On altered and/or bigger datasets,… cost-effective picture classification is accomplished, and enhanced image feature mapping is derived from related images in text metadata using CNNs. Given the limited association between feature labels and comparable (and/or unrelated) pictures, employing feature map representations is demonstrated to be cheaper and quicker, but it does not increase the quality of the image classifications, suggesting that this technique is not ideal for assessing quality. However, using the newly acquired learnt weights, the findings of the current study may inspire further research into alternative counterfeit detection methods. Overall, our study shows that metadata sampling and categorization need a highly disciplined scaling model, which can be scored by using a pre-trained model, and which may be further developed in future phases.
