{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:03:16Z","timestamp":1760241796195,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T00:00:00Z","timestamp":1536710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC1600605"],"award-info":[{"award-number":["2017YFC1600605"]}]},{"name":"BTBU Research Startup Project of Young Teachers","award":["QNJJ2017-15"],"award-info":[{"award-number":["QNJJ2017-15"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61273002"],"award-info":[{"award-number":["61273002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Construction of technological innovation and service capability - Basic scientific research service fee-innovation platform","award":["PXM2018_014213_000033"],"award-info":[{"award-number":["PXM2018_014213_000033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18093061","type":"journal-article","created":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T10:26:36Z","timestamp":1536747996000},"page":"3061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications"],"prefix":"10.3390","volume":"18","author":[{"given":"Jian-lei","family":"Kong","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen-ni","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-0077","authenticated-orcid":false,"given":"Xue-bo","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-yi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting-li","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-li","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Waser, L.T., Boesch, R., Wang, Z., and Ginzler, C. 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