Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo

Microscopic object recognition and analysis is very important in micromanipulation. Micromanipulation has been extensively used in many fields, e.g., micro-assembly operation, microsurgery, agriculture, and biological research. Conducting micro-object recognition in the in-situ measurement of tissue, e.g., in the ion flux measurement by moving an ion-selective microelectrode (ISME), is a complex problem. For living tissues growing at a rate, it remains a challenge to accurately recognize and locate an ISME to protect living tissues and to prevent an ISME from being damaged. Thus, we proposed a robust and fast recognition method based on local binary pattern (LBP) and Haar-like features fusion by training a cascade of classifiers using the gentle AdaBoost algorithm to recognize microscopic objects. Then, we could locate the electrode tip from the background with strong noise by using the Hough transform and edge extraction with an improved contour detection method. Finally, the method could be used to automatically and accurately calculate the relative distance between the two micro-objects in the microscopic image. The results show that the proposed method can achieve good performance in micro-object recognition with a recognition rate up to 99.14% and a tip recognition speed up to 14 frames/s at a resolution of 1360 x 1024. The max error of tip positioning is 6.10 mu m, which meets the design requirements of the ISME system. Furthermore, this study provides an effective visual guidance method for micromanipulation, which can facilitate automated micromanipulation research.