Maximum likelihood estimation for disk image parameters
This work addresses a specific computer vision task for disk parameter estimation, offering an incremental improvement in efficiency and accuracy.
The authors tackled the problem of estimating disk parameters (center and radius) from 2D images by developing a maximum likelihood approach that uses edge pixels and intensity gradients, resulting in a method with closed-form formulas that reduces computational resources compared to iterative algorithms.
We present a novel technique for estimating disk parameters (the centre and the radius) from its 2D image. It is based on the maximal likelihood approach utilising both edge pixels coordinates and the image intensity gradients. We emphasise the following advantages of our likelihood model. It has closed-form formulae for parameter estimating, requiring less computational resources than iterative algorithms therefore. The likelihood model naturally distinguishes the outer and inner annulus edges. The proposed technique was evaluated on both synthetic and real data.