437 resultados para knotting fingerprint
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Glycomacropeptide is a glycosilated fraction of bovine kappa-casein that remains soluble when milk is clotted by rennin. Determinations of milk sialic acid content are useful because its concentration reflects the amount of free GMP of milk. In normal milk these amounts are very low, 12 to 16 times lower than in sweet whey. Therefore, its determination may be applied to verify possible frauds with whey addictions, since it works as a fingerprint. With the description of a new spectrophotometric method for determination of free GMP (ANSM) occurred a simplification of procedures, being faster than others (HPLC method), without loss of accuracy. However, due to variations of glycosilation in kappa-casein between animals, during the lactation period, due to mastitis and yet due to proteolysis on milk, it was necessary to know these variations to interpret correctly the analytical results. It was analyzed 1,703 samples of producer's raw milk and 1,189 samples of processed milk (HTST and UHT). The results showed that normal milk from herd (producer's milk) have only small amounts of free GMP, with A470nm = 0.232±0.088 or 3.89±1.25 mg of sialic acid/L. The upper limit of this distribution was A = 0.496; thus every bigger value may represent a problem, being outside of normal distribution.
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Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Aquicultura - FCAV
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Ciências Biológicas (Botânica) - IBB
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Ciência da Computação - IBILCE
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Pós-graduação em Ciência da Computação - IBILCE
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Pós-graduação em Ciência da Computação - IBILCE
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Pós-graduação em Ciência da Computação - IBILCE