Impulse restoration template matching under geometric uncertainties

A common image processing problem is determining the location of an object using a template when the size and rotation of the target are unknown. Using a maximum likelihood formulation, localization is possible by computing a likelihood surface for a dense sampling of the size and rotation space. However, optimization is complicated by local minima and regions of small or zero gradient. We therefore demonstrate a technique which employs a library of templates starting from a smooth approximation and adding detail until the exact template is reached. Successively estimating the geometric parameters using these templates achieves the accuracy of the exact template while remaining within a well-behaved "bowl" in the search space which allows standard minimization techniques to be used.