Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications
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ID: 267806
2018
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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.
| Reference Key |
kong2018sensorssemi-supervised
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| Authors | Jian-lei Kong;Zhen-ni Wang;Xue-bo Jin;Xiao-yi Wang;Ting-li Su;Jian-li Wang;Kong, Jian-lei;Wang, Zhen-ni;Jin, Xue-bo;Wang, Xiao-yi;Su, Ting-li;Wang, Jian-li; |
| Journal | sensors |
| Year | 2018 |
| DOI |
10.3390/s18093061
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| URL | |
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