In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation
文献类型：期刊论文
| 作者 | Xia, CL ; Wang, LT; Chung, BK; Lee, JM
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| 刊名 | SENSORS
 |
| 出版日期 | 2015-08-01 |
| 卷号 | 15期号:8页码:20463-20479 |
| 关键词 | plant monitoring occlusions leaf detection mean shift center of divergence automatic initialization |
| ISSN号 | 1424-8220 |
| 产权排序 | [Xia, Chunlei] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Res Ctr Coastal Environm Engn & Technol Shandong, Yantai 264003, Peoples R China; [Xia, Chunlei; Wang, Longtan; Lee, Jang-Myung] Pusan Natl Univ, Sch Elect Engn, Busan 609735, South Korea; [Chung, Bu-Keun] Gyeongsangnam Do Agr Res & Extens Serv, Div Plant Environm, Jinju 660985, South Korea |
| 通讯作者 | Lee, JM (reprint author), Pusan Natl Univ, Sch Elect Engn, Busan 609735, South Korea. c.xia2009@gmail.com ; longtan7379@pusan.ac.kr ; bkchung@korea.kr ; jmlee@pusan.ac.kr |
| 英文摘要 | In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions. |
| 研究领域[WOS] | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 关键词[WOS] | MEAN-SHIFT ; MACHINE VISION ; KINECT SENSOR ; SYSTEM ; IMAGES ; IDENTIFICATION ; VEGETATION ; ALGORITHM ; ROBOT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000360906500134 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/9994]  |
| 专题 | 烟台海岸带研究所_山东省海岸带环境工程技术研究中心 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 |
| 推荐引用方式 GB/T 7714 | Xia, CL,Wang, LT,Chung, BK,et al. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation[J]. SENSORS,2015,15(8):20463-20479. |
| APA | Xia, CL,Wang, LT,Chung, BK,&Lee, JM.(2015).In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation.SENSORS,15(8),20463-20479. |
| MLA | Xia, CL,et al."In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation".SENSORS 15.8(2015):20463-20479. |
入库方式： OAI收割
来源：烟台海岸带研究所
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