Cargando…
Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model
Tracking and manipulating deformable linear objects (DLOs) has great potential in the industrial world. However, estimating the object's state is crucial and challenging, especially when dealing with heavy occlusion situations and physical properties of different objects. To address these probl...
Autores principales: | Wang, Taohan, Yamakawa, Yuji |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136076/ https://www.ncbi.nlm.nih.gov/pubmed/35645757 http://dx.doi.org/10.3389/fnbot.2022.886068 |
Ejemplares similares
-
Edge-Supervised Linear Object Skeletonization for High-Speed Camera
por: Wang, Taohan, et al.
Publicado: (2023) -
Robust tactile object recognition in open-set scenarios using Gaussian prototype learning
por: Zheng, Wendong, et al.
Publicado: (2022) -
A univocal definition of the neuronal soma morphology using Gaussian mixture models
por: Luengo-Sanchez, Sergio, et al.
Publicado: (2015) -
Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model
por: Wang, Jingkun, et al.
Publicado: (2022) -
GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model
por: Zhu, Weijin, et al.
Publicado: (2022)