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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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| 出版年: | Sensors (Basel) |
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| 主要な著者: | , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6165460/ https://ncbi.nlm.nih.gov/pubmed/30213109 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s18093061 |
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