Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202919418 ISBN 13: 9786202919418
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 61,85
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Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202919418 ISBN 13: 9786202919418
Lingua: Inglese
Da: preigu, Osnabrück, Germania
EUR 64,05
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. New Feature Descriptors for Content Based Image Retrieval | Development of New Feature Descriptors for Content Based Image Retrieval | K. Prasanthi Jasmine (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202919418 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Editore: LAP LAMBERT Academic Publishing Okt 2020, 2020
ISBN 10: 6202919418 ISBN 13: 9786202919418
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 76,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The proliferation of high quality and economical image sensors has precipitated renewed interest in the research community for developing efficient techniques to handle a large database. The concept of image retrieval came into existence and has been an active area of research aimed at addressing two major issues viz., database management and computer vision.The visual content descriptors are either global or local. A global descriptor represents the visual features of the whole image, whereas a local descriptor represents the visual features of regions or objects to describe the image. Further, these are subdivided into two categories, spatial and transform domain-based features. The approach makes use of pixels (a group of adjacent pixels) or gray values and the other makes use of transformed data of the gray image for feature calculation. 228 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing Okt 2020, 2020
ISBN 10: 6202919418 ISBN 13: 9786202919418
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 76,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The proliferation of high quality and economical image sensors has precipitated renewed interest in the research community for developing efficient techniques to handle a large database. The concept of image retrieval came into existence and has been an active area of research aimed at addressing two major issues viz., database management and computer vision.The visual content descriptors are either global or local. A global descriptor represents the visual features of the whole image, whereas a local descriptor represents the visual features of regions or objects to describe the image. Further, these are subdivided into two categories, spatial and transform domain-based features. The approach makes use of pixels (a group of adjacent pixels) or gray values and the other makes use of transformed data of the gray image for feature calculation.Books on Demand GmbH, Überseering 33, 22297 Hamburg 228 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202919418 ISBN 13: 9786202919418
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 77,82
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The proliferation of high quality and economical image sensors has precipitated renewed interest in the research community for developing efficient techniques to handle a large database. The concept of image retrieval came into existence and has been an active area of research aimed at addressing two major issues viz., database management and computer vision.The visual content descriptors are either global or local. A global descriptor represents the visual features of the whole image, whereas a local descriptor represents the visual features of regions or objects to describe the image. Further, these are subdivided into two categories, spatial and transform domain-based features. The approach makes use of pixels (a group of adjacent pixels) or gray values and the other makes use of transformed data of the gray image for feature calculation.