15 November 2017 An intelligent identification algorithm for the monoclonal picking instrument
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106054A (2017) https://doi.org/10.1117/12.2296329
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.
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Hua Yan, Hua Yan, Rongfu Zhang, Rongfu Zhang, Xujun Yuan, Xujun Yuan, Qun Wang, Qun Wang, } "An intelligent identification algorithm for the monoclonal picking instrument", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054A (15 November 2017); doi: 10.1117/12.2296329; https://doi.org/10.1117/12.2296329
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