9786135801347 - use of singular value decomposition for digital image watermarking di athalye, saurabh; athalye, sprooha; athalye, siddhi (5 risultati)
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Watermarking, is the process of embedding data into a multimedia cover, and can be used primarily for copyright protection and other purposes. Schemes that have recently been proposed modify the pixel values or Transform domain coe…fficients. The Singular Value Decomposition (SVD) is a practical numerical tool with applications in a number of signal processing fields including image compression. In an SVD-based watermarking scheme, the singular values of the cover image are modified to embed the watermark data. This method has been proposed an optimal SVD-based watermarking scheme that embeds the watermark in two steps. In the first step, the cover image is divided into smaller blocks and a piece of the watermark is embedded in each block. In the second step extracting the watermark from the watermarked image. Use of SVD provides robustness. It can be proved by testing it in presence of various noise attacks & general image processing operations. 56 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Athalye SaurabhSaurabh Athalye is from India. Qualification is M.E.(Electronics). Now he is doing his Phd. Area of interest is Digital Signal Processing. The author has 9 years of teaching experience a…t undergraduate level.Waterm.
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Da: buchversandmimpf2000, Emtmannsberg, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Watermarking, is the process of embedding data into a multimedia cover, and can be used primarily for copyright protection and other purposes. Schemes that have recently been proposed modify the pixel values or Transform domain coeffic…ients. The Singular Value Decomposition (SVD) is a practical numerical tool with applications in a number of signal processing fields including image compression. In an SVD-based watermarking scheme, the singular values of the cover image are modified to embed the watermark data. This method has been proposed an optimal SVD-based watermarking scheme that embeds the watermark in two steps. In the first step, the cover image is divided into smaller blocks and a piece of the watermark is embedded in each block. In the second step extracting the watermark from the watermarked image. Use of SVD provides robustness. It can be proved by testing it in presence of various noise attacks & general image processing operations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 60,51 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Watermarking, is the process of embedding data into a multimedia cover, and can be used primarily for copyright protection and other purposes. Schemes that have recently been proposed modify the pixel values or Transform domain coeffici…ents. The Singular Value Decomposition (SVD) is a practical numerical tool with applications in a number of signal processing fields including image compression. In an SVD-based watermarking scheme, the singular values of the cover image are modified to embed the watermark data. This method has been proposed an optimal SVD-based watermarking scheme that embeds the watermark in two steps. In the first step, the cover image is divided into smaller blocks and a piece of the watermark is embedded in each block. In the second step extracting the watermark from the watermarked image. Use of SVD provides robustness. It can be proved by testing it in presence of various noise attacks & general image processing operations.
