Suchergebnisse - "Object Detection, Segmentation and Categorization"

APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS

Autoren: Stefan de Jong Hilmy Baja Karsjen Tamminga et al.

Quelle: IEEE Robotics and Automation Letters. 7:11418-11425

Schlagwörter: Object detection, Robotics and Automation in Agriculture and Forestry, Agricultural Automation, Annotations, Task analysis, 0202 electrical engineering, electronic engineering, information engineering, Feature extraction, Three-dimensional displays, Training, Wearable sensors, Object Detection, Segmentation and Categorization, 02 engineering and technology, Deep Learning Methods

Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection

Autoren: Issa Mouawad Nikolas Brasch Fabian Manhardt et al.

Quelle: IEEE Robotics and Automation Letters. 7:8988-8995

Schlagwörter: FOS: Computer and information sciences, Computer Science - Robotics, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, object detection, segmentation and categorization, computer vision for transportation, 02 engineering and technology, Robotics (cs.RO)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2203.02193
https://hdl.handle.net/11567/1108894
https://doi.org/10.1109/lra.2022.3188882

APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS

Autoren: de Jong, Stefan Baja, Hilmy Tamminga, Karsjen et al.

Quelle: ISSN: 2377-3766

Index Begriffe: Agricultural Automation, Annotations, Deep Learning Methods, Feature extraction, Object Detection, Segmentation and Categorization, Object detection, Robotics and Automation in Agriculture and Forestry, Task analysis, Three-dimensional displays, Training, Wearable sensors, Article/Letter to editor

URL: https://edepot.wur.nl/577473
https://edepot.wur.nl/577473