Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment (Hardcover)

Xiaochun Wang

ISBN 10: 9811392161 / ISBN 13: 9789811392160

Edité par Springer Verlag, Singapore, Singapore, 2019

A propos de cet article

Hardcover. 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. This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9789811392160

Détails bibliographiques

• Titre: Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment (Hardcover)
• Éditeur: Springer Verlag, Singapore, Singapore
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 9811392161
• ISBN à 13 chiffres: 9789811392160
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

Synopsis

Part I Introduction1 Overview and Contributions2 Developments in Mobile Robot Localization Research3 A Computer Vision System for Visual Perception in Unknown Environments

Part II Unsupervised Learning4 Theory: Clustering5 Algorithm I: A Fast Approximate EMST Algorithm for High-Dimensional Image Data6 Algorithm II: An Efficient K-medoids Clustering Algorithm for Large Scale Image Data7 Algorithm III: Enhancing Complete Linkage Clustering via Boundary Point Detection8 Algorithm IV: A New Fast k-Nearest Neighbor-Based Clustering Algorithm
Part III Supervised Learning and Semi-Supervised Learning9 Theory: K-nearest Neighbor Classifiers10 Application I: A Fast Image Retrieval Method Based on Quantization Tree
11 Application II: A Fast Incremental Spectral Clustering Algorithm for Image Segmentation

Part IV Reinforcement Learning12 Theory: Human-Like Localization Inspired by a Hippocampal Memory Mechanism13 Application I: A Developmental Robotic Paradigm Using Working Memory Learning Mechanism14 Application II: An Autonomous Vision System Based Sensor-Motor Coordination for Open Space Detection15 Application III: Visual Percepts Learning for Mobile Robot Localization in An Indoor Environment16 Application IV: An Automatic Natural Scene Recognition Method for Mobile Robot Localization in An Outdoor Environment

À propos de l?auteur

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.