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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnVisual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, vide.
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Seller: preigu, Osnabrück, Germany
Buch. Condition: Neu. Visual Object Tracking with Deep Neural Networks | Pier Luigi Mazzeo (u. a.) | Buch | Gebunden | Englisch | 2019 | IntechOpen | EAN 9781789851571 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.Books on Demand GmbH, Überseering 33, 22297 Hamburg 206 pp. Englisch.
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.