Impulse restoration-based template-matching using the expectation-maximization algorithm

It has been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the object´s location. We develop solutions based on impulse restoration for the Gaussian noise case. We propose a new iterative approach, based on the expectation-maximization (EM) algorithm, that simultaneously estimates the background statistics and restores a delta function at the location of the template. We use localization-receiver-operating characteristics (LROC) curves to evaluate quantitatively the performance of this approach and compare it with existing methods. We present experimental results that demonstrate that impulse restoration is a powerful approach for detecting known objects in images severely degraded by noise. Our experiments demonstrate that accurate modeling and estimation of the background and noise statistics are essential to successful object detection