Automatic Micropipette Tip Detection and Focusing in Industrial Micro-Imaging System
Image processing is basic but important in micromanipulation technology. In this paper, we propose automatic micropipette tip detection and focusing algorithms for an economical and portable industrial micro-imaging system. At present, there are many image processing methods which have obtained good experimental results for microscopic vision. However, there are not suitable image processing methods for images with non-uniform brightness or at different magnification rates. The proposed detection method introduces morphological black hat operator to deal with the non-uniform background and obtains the position of the pipette tip accurately in both clear and blurred images. The proposed focusing method applies a multi-scale gradient transform algorithm to evaluate the clarity of the pipette at different magnifications. A focus strategy is then selected to realize auto-focusing according to the clarity feedback. Experimental results show that the proposed methods obtain better tip detection and clarity evaluation results when the micropipette tip changes from defocusing state to focusing state at different magnifications.
KeywordsMicro-manipulation Visual detection Clarity evaluation Autofocusing
This research was jointly supported by National Key R&D Program of China (2018YFB1304905), National Natural Science Foundation of China (U1813210, U1613220), Fundamental Research Funds for the Central Universities, Nankai University (63191177) and Natural Science Foundation of Tianjin (14JCZDJC31800, 14ZCDZGX00801).
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