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Sinopsis
This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.
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Acerca del autor
Xiaochun Wang received her BS degree from Beijing University and the PhD degree from the Department of Electrical Engineering and Computer Science, Vanderbilt University. She is currently an associate professor of School of Software Engineering at Xi’an Jiaotong University. Her research interests are in computer vision, signal processing, and pattern recognition.
Xia Li Wang received the PhD degree from the Department of Computer Science, Northwest University, China, in 2005. He is a faculty member in the Department of Computer Science, Changan University, China. His research interests are in computer vision, signal processing, intelligent traffic system, and pattern recognition.
D. Mitchell Wilkes received the BSEE degree from Florida Atlantic, and the MSEE and PhD degrees from Georgia Institute of Technology. His research interests include digital signal processing, image processing and computer vision, structurally adaptive systems, sonar,as well as signal modeling. He is a member of the IEEE and a faculty member at the Department of Electrical Engineering and Computer Science, Vanderbilt University. He is a member of the IEEE.
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This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Focuses on building bridges between natural and artificial computationPresents an object identification model and two different strategies for online learningEvaluates using a realistic scenario and delivers convincin. Nº de ref. del artículo: 449939059
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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Springer Nature Singapore Aug 2020, 2020
ISBN 10: 9811392196
ISBN 13: 9789811392191
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research. 352 pp. Englisch. Nº de ref. del artículo: 9789811392191
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811392196
ISBN 13: 9789811392191
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research. Nº de ref. del artículo: 9789811392191
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
Publicado por
Springer Nature Singapore, Springer Nature Singapore Aug 2020, 2020
ISBN 10: 9811392196
ISBN 13: 9789811392191
Nuevo
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. Neuware -This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch. Nº de ref. del artículo: 9789811392191
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
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