Robust recognition of scaled eigenimages through a hierarchical approach

Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results.