0000000001165720
AUTHOR
Philippe Réfrégier
Maximum likelihood for target location in the presence of substitutive noise .
We consider the optimal likelihood algorithm for the estimation of a target location when the images are corrupted by substitutive noise. We show the relationship between the optimal algorithm and the sliced orthogonal nonlinear generalized (SONG) correlation. The SONG correlation is based on the application of a linear correlation to corresponding binary slices of both the input scene and the reference object with appropriate weight factors. For a particular case, we show that the optimal strategy is a function of only the number of pixels for which the gray values in the noisy image match the ones of the reference image when the substitutive noise is uniformly distributed. This is exactly…
Target localization in the three-dimensional space by wavelength multiplexing.
A method to localize a target in the three-dimensional space is presented. Each different position of the target on the depth axis produces, when captured with a CCD camera, an image of a different size on its sensor plane. The size of this image depends only on the distance between the target and the camera. The use of a white light optical correlator that gives us a different response depending on the scale of the input image permits us to know the depth position of the particular target. The obtained results demonstrate the utility of the newly proposed method.