Human Signature Verification Using Machine Vision: Statistical and neural network approaches - Brossura
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Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. This boosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures. Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality.
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Human Signature Verification Using Machine Vision
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Noor Publishing Okt 2016, 2016
ISBN 10: 3330801549
ISBN 13: 9783330801547
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. This boosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures. Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality. 116 pp. Englisch. Codice articolo 9783330801547
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Human Signature Verification Using Machine Vision
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: El-Faki MohammedMohammed S. El-Faki is a Prof. at King Faisal University. He got PhD. and MSc. from Kansas State University, and BSc. from Khartoum University. Research interests: pattern recognition, process automation, quality con. Codice articolo 151242743
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Human Signature Verification Using Machine Vision: Statistical and neural network approaches
Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 116 pages. 8.66x5.91x0.27 inches. In Stock. Codice articolo 3330801549
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Human Signature Verification Using Machine Vision
Editore:
Noor Publishing Okt 2016, 2016
ISBN 10: 3330801549
ISBN 13: 9783330801547
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. This boosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures. Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. Codice articolo 9783330801547
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Human Signature Verification Using Machine Vision : Statistical and neural network approaches
Print on DemandDa: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. This boosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures. Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality. Codice articolo 9783330801547
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