Methods The proposed approach combines 2D spatial wavelet filtering with 1D temporal Karhunen-Loeve Transform (KLT). The KLT is first applied to create a series of “eigenimages” in which important signal information is concentrated into only a few eigenimages. Then a 2D spatial wavelet filter is applied to each of the individual eigenimages. An adaptive threshold is used to define the wavelet f...

We consider the problem of representing image matrices with a set of basis functions. One common solution for that problem is to first transform the 2D image matrices into 1D image vectors and then to represent those 1D image vectors with eigenvectors, as done in classical principal component analysis. In this paper, we adopt a natural representation for the 2D image matrices using eigenimages,...

We consider the problem of representing image matrices with a set of basis functions. One common solution for that problem is to first transform the 2D image matrices into 1D image vectors and then to represent those 1D image vectors with eigenvectors, as done in classical principal component analysis. In this paper, we adopt a natural representation for the 2D image matrices using eigenimages,...

The basic limitations of the standard appearance-based matching methods using eigenimages are nonrobust estimation of coefficients and inability to cope with problems related to outliers, occlusions, and varying background. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way the coefficients of the eigenimages are d...

In this paper we propose an eigenimage based superresolution reconstruction technique. Eigenimages of a database of several similar low resolution images are obtained and the given low resolution image is projected on to the eigenimages to compute the eigenimage coefficients. The eigenimages are then interpolated using any conventional interpolation method and approximated to the nearest orthon...

The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coeecients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coeecients of the eigenimages are determined. Instea...

We propose a method for identifying a painting in an image using eigenimages. We first pre-process the image to remove the background and then correct for skew and scaling. We then apply the method of eigenimages on de-meaned images. The algorithm correctly matched 97% of the images in a test database of 110 images, with an execution time of 3.6 s per image.

We propose a method for accelerating the matching and learning processes of the eigenspace method for rotation invariant template matching (RITM). To achieve efficient matching using eigenimages, it is necessary to learn 2D-Fourier transform of eigenimages before matching. Little attentions has been paid to speeding up the learning process, which is important for applications in which a templat...

Introduction: In this study, an eigenimage theory based on singular value decomposition (SVD) is proposed for SENSE [1]. Using SVD, a set of eigenimages can be generated from the reduced FOV images and the SENSE image can be represented as the linear combination of these eigenimages. As a result, the matrix inversion in SENSE is treated as the calculation of a set of linear coefficients for wei...