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Sensors 2018, 18(9), 3061; https://doi.org/10.3390/s18093061

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

1
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 20 August 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
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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. View Full-Text
Keywords: multi-sensor joint calibration; high-dimensional fusion data (HFD); supervoxel; Gaussian density peak clustering; sematic segmentation multi-sensor joint calibration; high-dimensional fusion data (HFD); supervoxel; Gaussian density peak clustering; sematic segmentation
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Kong, J.-L.; Wang, Z.-N.; Jin, X.-B.; Wang, X.-Y.; Su, T.-L.; Wang, J.-L. Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications. Sensors 2018, 18, 3061.

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