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Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement
Qin, Fangbo1,2; Shen, Fei1,2; Zhang, Dapeng1,2; Liu, Xilong1,2; Xu, De1,2
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
2018-08-01
Volume48Issue:8Pages:1348-1359
Corresponding AuthorXu, De(de.xu@ia.ac.cn)
AbstractThis paper proposes a suite of methods to realize high precision pose measurement in 3-D Cartesian space based on a multicamera microscopic vision system. Since it is inefficient to develop a specific image algorithm for each kind of object and the imaging condition might be unsatisfactory, we propose a method of contour primitives of interest extraction, which allows flexible reconfiguration for novel object image and owns robustness under different imaging conditions. The object is detected in grayscale image based on a template of contour primitives. Edges are extracted according to derivatives along the normal vectors of these contour primitives. The positions and directional derivatives of these edges are used for feature extraction and autofocus, respectively. The point features and line features extracted from multiview images are utilized to measure 3-D vectors and orientations, respectively, based on image Jacobian matrices. Cameras' linear motions are considered in the imaging model, so that the measurement range is expanded beyond the limitation of microscopes' shallow depths of field. The affine epipolar constraint and focused planes intersection constraint between cameras are applied to improve the real time performances of image feature extraction and multicamera autofocus, respectively. A series of experiments are conducted to verify the effectiveness of the proposed methods. The root mean square errors of pose measurement arc evaluated as 3 mu m in position and 0.05 degrees in orientation, while the measurement range is about 5000 mu m in position and 20 degrees in orientation.
KeywordGeometric constraint image feature extraction microscopic vision pose measurement precision assembly
DOI10.1109/TSMC.2017.2669219
WOS KeywordVISION-BASED CONTROL ; HOUGH TRANSFORM ; ALGORITHM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61227804] ; National Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61503378] ; National Natural Science Foundation of China[61673383]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Cybernetics
WOS IDWOS:000439358200011
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23595
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorXu, De
Affiliation1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Qin, Fangbo,Shen, Fei,Zhang, Dapeng,et al. Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2018,48(8):1348-1359.
APA Qin, Fangbo,Shen, Fei,Zhang, Dapeng,Liu, Xilong,&Xu, De.(2018).Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,48(8),1348-1359.
MLA Qin, Fangbo,et al."Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 48.8(2018):1348-1359.
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