Since the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, instead of an ensemble of an n–D realization. Although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to better results. In this paper, first a method is prop...

With the proliferation of cell phone cameras, new applications are being sought out by cell phone service providers. We explore a possibility for one such application that will provide image recognition for digitally captured images. Specifically, we investigate the applicability of eigenimages for the recognition of pieces of art that were captured with the Nokia N93 camera-phone.

Radiochromic film with a dye incorporated into the radiation sensitive layer [Gafchromic EBT2, Ashland, Inc.] may be digitized by a color transparency scanner, digitally processed, and calibrated so that a digital image in units of radiation absorbed dose is obtained. A transformation from raw scanner values to dose values was developed based upon a principal component analysis of the optical d...

In this work we consider the problem of determining efficient scanning strategies for adaptive networks of radars used to monitor meteorological events. While most meteorological radars traditionally scan 360 degrees, repeatedly, more accurate readings can be obtained if adaptive sector scanning is performed. We propose a scanning strategy based on analyzing eigenimages computed given a set of ...

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for speci...

Abstract: In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations with n repeated measurements. While principal component analysis (PCA) is commonly applied to find the patterns, the eigenimages produced from PCA may be noisy or exhibit patterns that are not physically meaningful when p is large relative ...

From the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, (instead of an ensemble of an n–D realization). Although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to more realistic results. In this paper, based on the proposed method fo...

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 approa...