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• W2891675219 abstract "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 successive steps: firstly, the relationship of multi-sensor coordinates is jointly calibrated to form higher-dimensional fusion data. Then, spot-divergence supervoxels representing the size-change property are given to produce feature vectors covering comprehensive information of multi-sensors at a time. Finally, the Gaussian density peak clustering is proposed to segment supervoxels into sematic objects in the semi-supervised way, which non-requires parameters preset in manual. It is demonstrated that the proposed framework achieves a balancing act both for supervoxel generation and sematic segmentation. Comparative experiments show that the well performance of segmenting various objects in terms of segmentation accuracy (F-score up to 95.6%) and operation time, which would improve intelligent capability of AFMs." @default.
• W2891675219 created "2018-09-27" @default.
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• W2891675219 date "2018-09-12" @default.
• W2891675219 modified "2023-09-26" @default.
• W2891675219 title "Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications" @default.
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• W2891675219 doi "https://doi.org/10.3390/s18093061" @default.
• W2891675219 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6165460" @default.
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