Real-World ISAR Object Recognition and Relation Discovery Using Deep Relation Graph Learning

Real-world inverse synthetic aperture radar (ISAR) object recognition is the most critical and challenging problem in computer vision tasks. In this paper, an efficient real-world ISAR object recognition and relation discovery method are proposed, based on deep relation graph learning. It not only h...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2019, Vol.7, p.43906-43914
Hauptverfasser:	Xue, Bin, Tong, Ningning
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer vision Convolution Convolutional neural networks Deep relation graph learning Deformation dilated deformable Feature extraction Feature maps Formability Inverse synthetic aperture radar Learning multi-scale relational-regularized convolutional sparse learning Object recognition real-world object recognition Semantics Strain
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!