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Aggiungi al carrelloCondizione: New. pp. 598.
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Dr. Pei-Gee Ho dissertation | MULTIVARIATE TIME SERIES MODEL BASED SUPPORT VECTOR MACHINE FOR MULTICLASS REMOTE SENSING IMAGE CLASSIFICATION AND REGION SEGMENTATION | Pei-Gee Ho | Taschenbuch | 124 S. | Englisch | 2009 | LAP LAMBERT Academic Publishing | EAN 9783838303529 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: Buchpark, Trebbin, Germania
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world¿s daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: Mispah books, Redhill, SURRE, Regno Unito
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Jun 2009, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world's daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time. 124 pp. Englisch.
Lingua: Inglese
Editore: LAP Lambert Academic Publishing, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: moluna, Greven, Germania
EUR 48,50
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world s daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer i.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Jun 2009, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world's daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2009
ISBN 10: 3838303520 ISBN 13: 9783838303529
Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Satellite and airborne Remote Sensing for observing the earth surface, land monitoring and geographical information systems control are issues in world's daily life. The source of information was primarily acquired by imaging sensors and spectroradiometer in remote sensing multi-spectral image stack format. The contextual information between pixels or pixel vectors is characterized by a time series model for image processing in the remote sensing. Due to the nature of remote sensing images such as SAR and TM which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements are formulated. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied. The 2-D multivariate time series model is particularly suitable to capture the rich contextual information in single and multiple images at the same time.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 141,75
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: moluna, Greven, Germania
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnThe field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 129,00
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text.This new edition of 'Advanced Image Segmentation' is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years. The book presented chapters that highlight frontier works in image information processing. 128 pp. Englisch.
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloBuch. Condizione: Neu. Advances in Image Segmentation | Pei-Gee Ho | Buch | 128 S. | Englisch | 2012 | IntechOpen | EAN 9789535108177 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.