Visual Tracking With Motion Distortion Removal for Nanomanipulation Inside SEM

The article investigates the problem of visual tracking of moving objects in nanomanipulation inside a scanning electron microscope (SEM). Image noise is a primary concern when dealing with the problem, which includes the inherent statistical noise and that induced by the motion distortion. A visual tracking method with SEM image denoising algorithm is proposed. The denoising algorithm is well incorporated with robot motion by innovatively leveraging the image Jacobian matrix technique. The denoising algorithm removes image noise and provides a more realistic image. On this basis, template matching is utilized to achieve visual tracking on the image plane. Experimental results show that the visual tracking performance on the image plane was well improved by 31.4% for the point feature, 60.6% for the line feature, and 52.1% for the area feature in terms of root mean square error (RMSE) compared to that without motion distortion removal. Comparison experiments validate the state-of-the-art performance achieved by the proposed method and thus the superiority.