A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal components in high{ dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d{pixel products in images. We give the derivation of the method and present experimenta...

Images captured by a camera through glass often have reflection superimposed on the transmitted background. Among existing methods for reflection separation, multi-view methods are the most convenient to apply because they require the user to just take multiple images of a scene at varying viewing angles. Some of these methods are restricted to the simple case where the background scene and ref...

With the launch of the German TerraSAR-X system in June 2007, a new generation of high-resolution spaceborne Synthetic Aperture Radar (SAR) data is available; which facilitates a spatially and thematically detailed SAR scene analysis. In fact, the high resolution of TerraSAR-X enables scene on land cover, such as urban areas, deserts, forests and fields, to be accurately mapped. Among the sever...

How to provide reasonable hardware resources and improve the efficiency of soft-ware is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Principal component analysis (PCA) is used to reduce the dimen...

| In this paper we propose two feature extraction techniques, termed DCT-mod and DCT-delta, for use in an illumination invariant face based identity veri cation system. We compare the performance of the proposed techniques against two standard methods: Principal Component Analysis (PCA) and the 2-D Discrete Cosine Transform (DCT). Experiments on the VidTIMIT database support the use of the prop...

'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method, where a kernel function implicitly defines a nonlinear transformation into a feature space wherein standard PCA is performed. Unfortunately, the technique is not 'sparse', since the components thus obtained are expressed in terms of kernels associated with every trainin...

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...

In this paper, we have presented a system for isolated curved Gurmukhi character recognition using projection of gradient. We have used projection of gradient features to extract the features of the character. The method relies upon the application of a projection based feature extraction using Radon transform, on both the original image and a set of produced images corresponding to different g...

Multivariate Gaussian data is completely characterized by its mean and covariance, yet modern non-Gaussian data makes higher-order statistics such as cumulants inevitable. For univariate data, the third and fourth scalar-valued cumulants are relatively well-studied as skewness and kurtosis. For multivariate data, these cumulants are tensor-valued, higher-order analogs of the covariance matrix c...