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

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Veröffentlicht in:Pattern Recognition Jg. 132; S. 108956
Hauptverfasser: Zhihan Lv, Ling Qiao, Shuo Yang, Jinhua Li, Haibin Lv, Francesco Piccialli
Format: Journal Article
Sprache:Japanisch
Veröffentlicht: Elsevier BV 01.12.2022
Schlagworte:
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-Zugang:Volltext
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Beschreibung
ISSN:0031-3203
DOI:10.1016/j.patcog.2022.108956