Maximum likelihood estimation for disk image parameters
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We present a novel technique for estimating disc parameters from its 2D image. It is based on the maximal likelihood approach utilising both edge coordinates and the image intensity gradients. We emphasise the following advantages of our likelihood model. It has closed-form formulae for parameter estimating, therefore requiring less computational resources than iterative algorithms. The likelihood model naturally distinguishes the outer and inner annulus edges. The proposed technique was evaluated on both synthetic and real data.
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