Li Jinxing (14 results)

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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Information Fusion | Machine Learning Methods | Jinxing Li (u. a.) | Taschenbuch | xxvi | Englisch | 2023 | Springer | EAN 9789811689789 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the big data era, increasinginformationcan be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all availableinformationat multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurateinformation and to maximize performance in a range of applications.This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy, Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc. This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research. 288 pp. Englisch.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the big data era, increasinginformationcan be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all availableinformationat multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurateinformation and to maximize performance in a range of applications.This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy, Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc. This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research. 288 pp. Englisch.…

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications.This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy,Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc.This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 288 pp. Englisch.…

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications.This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy,Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc.This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 288 pp. Englisch.…