Memory‐augmented neural networks based dynamic complex image segmentation in digital twins for self‐driving vehicle

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Bibliographic Details
Published in:	Pattern Recognition Vol. 132; p. 108956
Main Authors:	Zhihan Lv, Ling Qiao, Shuo Yang, Jinhua Li, Haibin Lv, Francesco Piccialli
Format:	Journal Article
Language:	Japanese
Published:	Elsevier BV 01.12.2022
Subjects:	Artificial intelligence Artificial neural network Automotive Engineering Autonomous Vehicle Technology and Safety Systems Beräkningsmatematik Biofysik Biology Biophysics Civil engineering Classification of Brain Tumor Type and Grade Complex image Complex networks Computational Mathematics Computer science Computer Sciences Computer Systems Computer vision Computer Vision and Pattern Recognition Computer Vision and Robotics (Autonomous Systems) Context (archaeology) Datavetenskap (datalogi) Datorseende och robotik (autonoma system) Datorsystem Deep learning Deep Learning in Computer Vision and Image Recognition Deep neural networks Digital image digital twin Digital twins Driver Assistance Systems E-learning Engineering FOS: Civil engineering FOS: Mechanical engineering Image (mathematics) Image enhancement Image processing Image Recognition Image segmentation Images segmentations Intelligent transportation Intelligent transportation system Lane Detection Learning algorithms Life Sciences Long short-term memory LSTM Machine learning Memory-augmented neural network Memory-augmented neural networks Neural-networks Neurology Neuroscience Operating system Paleontology Pascal (unit) Physical Sciences Programming language Segmentation Self drivings Self-driving Semantic Segmentation Speedup
ISSN:	0031-3203
Online Access:	Get full text
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