An intelligent identification algorithm for the monoclonal picking instrument

Hua Yan; Rongfu Zhang; Xujun Yuan; Qun Wang

doi:10.1117/12.2296329

15 November 2017 An intelligent identification algorithm for the monoclonal picking instrument

Hua Yan, Rongfu Zhang, Xujun Yuan, Qun Wang

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.

Citation Download Citation

Hua Yan, Rongfu Zhang, Xujun Yuan, and 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); https://doi.org/10.1117/12.2296329

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