Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications

In this paper, a novel semi-supervised segmentation framework based on a spot-divergence supervoxelization of multi-sensor fusion data is proposed for autonomous forest machine (AFMs) applications in complex environments. Given the multi-sensor measuring system, our framework addresses three success...

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Detalles Bibliográficos
Autores principales: Kong, Jian-lei, Wang, Zhen-ni, Jin, Xue-bo, Wang, Xiao-yi, Su, Ting-li, Wang, Jian-li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165460/
https://www.ncbi.nlm.nih.gov/pubmed/30213109
http://dx.doi.org/10.3390/s18093061

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165460/
https://www.ncbi.nlm.nih.gov/pubmed/30213109
http://dx.doi.org/10.3390/s18093061

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