Synopsis
In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation.
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Forgery detection using noise variance estimation and HOG features
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Scholars' Press Mai 2018, 2018
ISBN 10: 6202311452
ISBN 13: 9786202311458
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation. 72 pp. Englisch. Seller Inventory # 9786202311458
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Forgery detection using noise variance estimation and HOG features
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Forgery detection using noise variance estimation and HOG features
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Forgery detection using noise variance estimation and HOG features
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Forgery detection using noise variance estimation and HOG features
Published by
Scholars' Press Mai 2018, 2018
ISBN 10: 6202311452
ISBN 13: 9786202311458
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch. Seller Inventory # 9786202311458
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Forgery detection using noise variance estimation and HOG features
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation. Seller Inventory # 9786202311458
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Forgery detection using noise variance estimation and HOG features
Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Forgery detection using noise variance estimation and HOG features | Savita Walia (u. a.) | Taschenbuch | Englisch | 2018 | Scholars' Press | EAN 9786202311458 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 118708146