Robust Recognition of Scaled Eigenimages through a Hierarchical Approach

Recently, we have proposed a new approach to estimation of the coeecients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise 4, 5]. In this paper we show that our method for estimating the coeecients can be applied to convolved and subsampled images yielding the same value of the coeecients. This enables an eecient multiresolution approach, where the values of the coeecients 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 con-rm our theoretical results.