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Segmentation and Separation of Overlapped Latent Fingerprints: Algorithms, Techniques, and Datasets (SpringerBriefs in Computer Science) - Tapa blanda
Sinopsis
This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.
This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.
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De la contraportada
This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.
This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.
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- EditorialSpringer
- Año de publicación2019
- ISBN 10 3030233634
- ISBN 13 9783030233631
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas64
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
Publicado por
Springer International Publishing, 2019
ISBN 10: 3030233634
ISBN 13: 9783030233631
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Calificación del vendedor: 5 de 5 estrellas
Condición: Sehr gut. Zustand: Sehr gut | Seiten: 64 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 35691401/2
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Segmentation and Separation of Overlapped Latent Fingerprints
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Springer International Publishing Nov 2019, 2019
ISBN 10: 3030233634
ISBN 13: 9783030233631
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path for learning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information. 64 pp. Englisch. Nº de ref. del artículo: 9783030233631
Cantidad disponible: 2 disponibles
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Segmentation and Separation of Overlapped Latent Fingerprints: Algorithms, Techniques, and Datasets (SpringerBriefs in Computer Science)
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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Springer International Publishing, Springer International Publishing, 2019
ISBN 10: 3030233634
ISBN 13: 9783030233631
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path forlearning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information. Nº de ref. del artículo: 9783030233631
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Segmentation and Separation of Overlapped Latent Fingerprints
Publicado por
Springer International Publishing, 2019
ISBN 10: 3030233634
ISBN 13: 9783030233631
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (. Nº de ref. del artículo: 334026376
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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Segmentation and Separation of Overlapped Latent Fingerprints : Algorithms, Techniques, and Datasets
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