Published by Singapore, Springer., 2018
ISBN 10: 9811085684 ISBN 13: 9789811085680
Language: English
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
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Add to basketXII, 380 p. Softcover. 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. Advances in Intelligent Systems and Computing. Volume 705. Sprache: Englisch.
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
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Published by Taylor & Francis Ltd, 2023
ISBN 10: 1032416319 ISBN 13: 9781032416311
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 243.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 1016 pages. 11.68x8.25x2.13 inches. In Stock.
Seller: Buchpark, Trebbin, Germany
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 239 | Sprache: Englisch | Produktart: Bücher.
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Published by Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10: 3031598105 ISBN 13: 9783031598104
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 153.08
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.This book provides numerous ways that deep learners can use for logo recognition, including:Deep learning-based end-to-end trainable architecture for logo detectionWeakly supervised logo recognition approach using attention mechanismsAnchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world imagesUnsupervised logo detection that takes into account domain-shift issues from synthetic to real-world imagesApproach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
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Published by Springer Nature Switzerland, Springer International Publishing, 2024
ISBN 10: 3031718208 ISBN 13: 9783031718205
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. It covers state-of-the-art machine learning techniques and large language models using multimodal sensors. Enhancing the quality of life for individuals with special needs, particularly the elderly, is a key focus in Active and Assisted Living (AAL) research. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems. This book offers insights into technologies transforming AAL for researchers, students, and practitioners. It is a valuable resource for developing responsive and personalized support systems that enhance life quality in smart environments. It is also essential for advancing the understanding of machine learning and sensor technologies in AAL and emotion recognition.
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