A Robust Multiscale Algorithm for Motion Measurement With High Accuracy

The computer micro-vision technique plays a vital role in many existing methods of motion measurement in precision engineering, which its measurement accuracy and speed perform very well. Gradient-based techniques represent a very popular class of approaches to measuring motions. A robust multiscale algorithm of hierarchical estimation for gradient-based motion estimation is proposed in this paper using a combination of robust statistical method and multiscale technique. In such a multiscale approach of hierarchical estimation, motion at each level of the pyramid is estimated using different gradient filters. The iterative multiscale estimation begins by using 5-tap Central filter, and it is switched to 9-tap Timoner filter after a few iterations. In addition, robust M-estimators are applied at each level of the pyramid in order to overcome the problem of the outliers caused by illumination variations and motion discontinuities in motion estimates. Experimental simulations show that the new algorithm not only provides an improvement in estimator accuracy, but also achieves computational speedups for motion measurement of MEMS image.