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题名:
Plant leaf detection using modified active shape models
其他题名: SERS-based immunoassay of tumor marker VEGF using DNA aptamers and silica-encapsulated hollow gold nanospheres.pdf
作者: Xia ChunLei(夏春雷)1,2,5; Lee, Jang-Myung1; Li, Yan1; Song, Yoo-Han3; Chung, Bu-Keun4; Chon, Tae-Soo2
刊名: BIOSYSTEMS ENGINEERING
ISSN号: 1537-5110
出版日期: 2013-09-01
卷号: 116, 期号:1, 页码:23-35
关键词: RECOGNITION ; CLASSIFICATION ; SEGMENTATION ; IDENTIFICATION ; ALGORITHM ; FEATURES ; IMAGES
学科分类: Agricultural Engineering ; Agriculture, Multidisciplinary
产权排序: [Xia, Chunlei; Lee, Jang-Myung; Li, Yan] Pusan Natl Univ, Sch Elect Engn, Pusan 609735, South Korea; [Xia, Chunlei; Chon, Tae-Soo] Pusan Natl Univ, Dept Biol Sci, Pusan 609735, South Korea; [Song, Yoo-Han] Gyeongsang Natl Univ, Dept Appl Biol & Environm Sci, Jinju, South Korea; [Chung, Bu-Keun] Gyeongnam Agr Res & Extens Serv, Div Plant Environm, Jinju, South Korea; [Xia, Chunlei] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
通讯作者: Chon, TS (reprint author), Pusan Natl Univ, Dept Biol Sci, Pusan 609735, South Korea. tschon@pusan.ac.kr
中文摘要: We propose an in situ detection method of multiple leaves with overlapping and occlusion in greenhouse conditions. Initially a multilayer perceptron (MLP) is used to classify partial boundary images of pepper leaves. After the partial leaf boundary detection, active shape models (ASMs) are subsequently built to employ the images of entire leaves based on a priori knowledge using landmark. Two deformable models were developed with pepper leaves: Boundary-ASM and MLP-ASM. Matching processes are carried out by deforming the trained leaf models to fit real leaf images collected in the greenhouse. MLP-ASM detected 76.7 and 87.8% of overlapping and occluded pepper leaves respectively, while Boundary-ASM showed detection rates of 63.4 and 76.7%. The detection rates by the conventional ASM were 23.3 and 29.3%. The leaf models trained with pepper leaves were further tested with leaves of paprika, in the same family but with more complex shapes (e.g., holes and rolling). Although the overall detection rates were somewhat lower than those for pepper, the rates for the occluded and overlapping leaves of paprika were still higher with MLP-ASM (ranging from 60.4 to 76.7%) and Boundary-ASM (ranging from 50.5 to 63.3%) than using the conventional active shape model (from 21.6 to 30.0%). The modified active shape models with the boundary classifier could be an efficient means for detecting multiple leaves in field conditions. (c) 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.
英文摘要: We propose an in situ detection method of multiple leaves with overlapping and occlusion in greenhouse conditions. Initially a multilayer perceptron (MLP) is used to classify partial boundary images of pepper leaves. After the partial leaf boundary detection, active shape models (ASMs) are subsequently built to employ the images of entire leaves based on a priori knowledge using landmark. Two deformable models were developed with pepper leaves: Boundary-ASM and MLP-ASM. Matching processes are carried out by deforming the trained leaf models to fit real leaf images collected in the greenhouse. MLP-ASM detected 76.7 and 87.8% of overlapping and occluded pepper leaves respectively, while Boundary-ASM showed detection rates of 63.4 and 76.7%. The detection rates by the conventional ASM were 23.3 and 29.3%. The leaf models trained with pepper leaves were further tested with leaves of paprika, in the same family but with more complex shapes (e.g., holes and rolling). Although the overall detection rates were somewhat lower than those for pepper, the rates for the occluded and overlapping leaves of paprika were still higher with MLP-ASM (ranging from 60.4 to 76.7%) and Boundary-ASM (ranging from 50.5 to 63.3%) than using the conventional active shape model (from 21.6 to 30.0%). The modified active shape models with the boundary classifier could be an efficient means for detecting multiple leaves in field conditions. (c) 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.
研究领域[WOS]: Agriculture
关键词[WOS]: RECOGNITION ; CLASSIFICATION ; SEGMENTATION ; IDENTIFICATION ; ALGORITHM ; FEATURES ; IMAGES
文章类型: Article
收录类别: SCI
原文出处: 查看原文
语种: 英语
WOS记录号: WOS:000323856000003
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.yic.ac.cn/handle/133337/7055
Appears in Collections:山东省海岸带环境工程技术研究中心_期刊论文

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作者单位: 1.Pusan Natl Univ, Sch Elect Engn, Pusan 609735, South Korea
2.Pusan Natl Univ, Dept Biol Sci, Pusan 609735, South Korea
3.Gyeongsang Natl Univ, Dept Appl Biol & Environm Sci, Jinju, South Korea
4.Gyeongnam Agr Res & Extens Serv, Div Plant Environm, Jinju, South Korea
5.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China

Recommended Citation:
Xia, Chunlei,Lee, Jang-Myung,Li, Yan,et al. Plant leaf detection using modified active shape models[J]. BIOSYSTEMS ENGINEERING,2013,116(1):23-35.
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