Image-processing thresholding algorithms are extended segmentation tools that are suitable for tracking applications. The centroid of the tracked image distribution is a good point of reference for the location of the image. We describe a new thresholding technique that is based on the estimation of the optimum threshold for achieving minimal variance in the centroid of the processed image. Experimental proofs for evaluating the technique’s performance are given. The direct extension of these results to Shack–Hartmann wave-front sensors is also shown.
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