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Sinopsis
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
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This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
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- EditorialSpringer
- Año de publicación2014
- ISBN 10 3642548504
- ISBN 13 9783642548505
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas216
- EditorChen Yen-Wei, C. Jain Lakhmi
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Springer Berlin Heidelberg, 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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Subspace methods for pattern recognition in intelligent environment.
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Heidelberg/New York, NY/Dordrecht/London/Berlin, Springer., 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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24 cm. XVI, 198 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in computational intelligence ; Vol. 552. Sprache: Englisch. Nº de ref. del artículo: 6133IB
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Springer Berlin Heidelberg, 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research on the theoretical foundations and applications of subspace methods for pattern recognition using intelligent techniquesActive Shape Model and Its Application to Face Alignment.-Condition Rela. Nº de ref. del artículo: 5064214
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Springer Berlin Heidelberg Apr 2014, 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. 216 pp. Englisch. Nº de ref. del artículo: 9783642548505
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Springer Berlin Heidelberg, 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. Nº de ref. del artículo: 9783642548505
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Subspace Methods for Pattern Recognition in Intelligent Environment (Studies in Computational Intelligence, 552)
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2014, 2014
ISBN 10: 3642548504
ISBN 13: 9783642548505
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Buch. Condición: Neu. Neuware -This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch. Nº de ref. del artículo: 9783642548505
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Subspace Methods for Pattern Recognition in Intelligent Environment
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Subspace Methods for Pattern Recognition in Intelligent Environment (Studies in Computational Intelligence, 552)
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Subspace Methods for Pattern Recognition in Intelligent Environment (Studies in Computational Intelligence, 552)
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