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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
Xia, Chunlei1,2,5; Lee, Jang-Myung1; Li, Yan1; Song, Yoo-Han3; Chung, Bu-Keun4; Chon, Tae-Soo2; Chon, TS (reprint author), Pusan Natl Univ, Dept Biol Sci, Pusan 609735, South Korea. tschon@pusan.ac.kr
2013-09-01
发表期刊BIOSYSTEMS ENGINEERING
ISSN1537-5110
卷号116期号:1页码:23-35
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
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.; 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.
关键词Recognition Classification Segmentation Identification Algorithm Features Images
学科领域Agricultural Engineering ; Agriculture, Multidisciplinary
项目资助者Korea Institute of Planning and Evaluation for Technology of Food, Agriculture, Forestry, and Fisheries [108929033HD120]
URL查看原文
收录类别SCI
关键词[WOS]RECOGNITION ; CLASSIFICATION ; SEGMENTATION ; IDENTIFICATION ; ALGORITHM ; FEATURES ; IMAGES
文章类型Article
语种英语
资助项目海岸带环境工程技术研究中心
WOS研究方向Agriculture
WOS记录号WOS:000323856000003
引用统计
被引频次:13[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cn/handle/133337/7055
专题山东省海岸带环境工程技术研究中心
通讯作者Chon, TS (reprint author), Pusan Natl Univ, Dept Biol Sci, Pusan 609735, South Korea. tschon@pusan.ac.kr
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
第一作者单位中国科学院烟台海岸带研究所
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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.
APA Xia, Chunlei.,Lee, Jang-Myung.,Li, Yan.,Song, Yoo-Han.,Chung, Bu-Keun.,...&Chon, TS .(2013).Plant leaf detection using modified active shape models.BIOSYSTEMS ENGINEERING,116(1),23-35.
MLA Xia, Chunlei,et al."Plant leaf detection using modified active shape models".BIOSYSTEMS ENGINEERING 116.1(2013):23-35.
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