Automatic Targeting of Plant Cells via Cell Segmentation and Robust Scene-Adaptive Tracking

© 2019 IEEE. Automatic targeting of plant cells to perform tasks like extraction of chloroplast is often desired in the study of plant biology. Hence, this paper proposes an improved cell segmentation method combined with a robust tracking algorithm for vision-guided micromanipulation in plant cells...

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Bibliographic Details
Main Authors: Paranawithana, Ishara (Author), Chau, Zhong Hoo (Author), Yang, Liangjing (Author), Chen, Zhong (Author), Youcef-Toumi, Kamal (Author), Tan, U-Xuan (Author)
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2021-01-06T16:43:12Z.
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Online Access:Get fulltext
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100 1 0 |a Paranawithana, Ishara  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Mechanical Engineering  |e contributor 
700 1 0 |a Chau, Zhong Hoo  |e author 
700 1 0 |a Yang, Liangjing  |e author 
700 1 0 |a Chen, Zhong  |e author 
700 1 0 |a Youcef-Toumi, Kamal  |e author 
700 1 0 |a Tan, U-Xuan  |e author 
245 0 0 |a Automatic Targeting of Plant Cells via Cell Segmentation and Robust Scene-Adaptive Tracking 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2021-01-06T16:43:12Z. 
856 |z Get fulltext  |u https://hdl.handle.net/1721.1/129074 
520 |a © 2019 IEEE. Automatic targeting of plant cells to perform tasks like extraction of chloroplast is often desired in the study of plant biology. Hence, this paper proposes an improved cell segmentation method combined with a robust tracking algorithm for vision-guided micromanipulation in plant cells. The objective of this work is to develop an automatic plant cell detection and localization technique to complete the automated workflow for plant cell manipulation. The complex structural properties of plant cells make both segmentation of cells and visual tracking of the microneedle immensely challenging, unlike single animal cell applications. Thus, an improved version of watershed segmentation with adaptive thresholding is proposed to detect the plant cells without the need for staining of the cells or additional tedious preparations. To manipulate the needle to reach the identified centroid of the cells, tracking of the needle tip is required. Visual and motion information from two data sources namely, template tracking and projected manipulator trajectory are combined using score-based normalized weighted averaging to continuously track the microneedle. The selection of trackers is influenced by their complementary nature as the former and latter are individually robust against physical and visual uncertainties, respectively. Experimental results validate the effectiveness of the proposed method by detecting plant cell centroids accurately, tracking the microneedle constantly and reaching the plant cell of interest despite the presence of visual disturbances. 
546 |a en 
655 7 |a Article 
773 |t IEEE International Conference on Robotics and Automation