Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment - Softcover

Wang, Xiaochun; Wang, Xiali; Wilkes, Don Mitchell

 
9789811392191: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment
Softcover
ISBN 10: 9811392196 ISBN 13: 9789811392191
Verlag: Springer, 2020

Inhaltsangabe

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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Über die Autorin bzw. den 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.

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2020
Sprache
Englisch
ISBN 10 
9811392196
ISBN 13 
9789811392191
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
352
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

Lengericher Landstr. 19
Osnabrück
Niedersachsen
49078
Deutschland

Weitere beliebte Ausgaben desselben Titels

9789811392160: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment

Vorgestellte Ausgabe

ISBN 10:  9811392161 ISBN 13:  9789811392160
Verlag: Springer, 2019
Hardcover