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

Uloženo v:

Podrobná bibliografie
Vydáno v:	Pattern Recognition Ročník 132; s. 108956
Hlavní autoři:	Zhihan Lv, Ling Qiao, Shuo Yang, Jinhua Li, Haibin Lv, Francesco Piccialli
Médium:	Journal Article
Jazyk:	japonština
Vydáno:	Elsevier BV 01.12.2022
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
ISSN:	0031-3203
DOI:	10.1016/j.patcog.2022.108956