Template matching based object recognition with unknown geometric parameters

Abstract—In this paper, we examine the problem of locating an object in an image when size and rotation are unknown. Previous work has shown that with known geometric parameters, an image restoration method can be useful by estimating a delta function at the object location. When the geometric parameters are unknown, this method becomes impractical because the likelihood surface to be minimized across size and rotation has numerous local minima and areas of zero gradient. In this paper, we propose a new ap-proach where a smooth approximation of the template is used to minimize a well-behaved likelihood surface. A coarse-to-fine ap-proximation of the original template using a diffusion-like equa-tion is used to create a library of templates. Using this library, we can successively perform minimizations which are locally well-be-haved. As detail is added to the template, the likelihood surface gains local minima, but previous estimates place us within a well-behaved “bowl ” around the global minimum, leading to an accu-rate estimate. Numerical experiments are shown which verify the value of this approach for a wide range of values of the geometric parameters. I