In this paper, we describe a new technique for expressive and realistic speech animation. We use an optical tracking system that extracts the 3D positions of markers attached at the feature point locations to capture the movements of the face of a talking person. We use the feature points as defined by the MPEG-4 standard. We then form a vector space representation by using the Principal Compon...

This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (i...

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or de...

Feature selection has received considerable attention in various areas as a way to select informative features and to simplify the statistical model through dimensional reduction. One of the most widely used methods for dimensional reduction includes principal component analysis (PCA). Despite its popularity, PCA suffers from a lack of interpretability of the original feature because the reduce...

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover n...

The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The ...

A principal component method for multivariate functional data is proposed. Data can be arranged in a matrix whose elements are functions so that for each individual a vector of p functions is observed. This set of p curves is reduced to a small number of transformed functions, retaining as much information as possible. The criterion to measure the information loss is the integrated variance. Un...

Selection of a good initial approximation is a well known problem for all iterative methods of data approximation, from k -means to Self-Organizing Maps (SOM) and manifold learning. The quality of the resulting data approximation depends on the initial approximation. Principal components are popular as an initial approximation for many methods of nonlinear dimensionality reduction because its c...

remotely sensed imagery is proving to be a useful tool to estimate water depths in coastalzones. bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors byusing different combinations of spectral bands. in this research, images of absolute bathymetry using twodifferent but related methods in a region in the southern caspian sea coasts has been produced. th...

in this article, the effect of operating conditions, such as temperature, gas hourly space velocity (ghsv), ch4/o2 ratio and diluents gas (mol% n2) on ethylene production by oxidative coupling of methane (ocm) in a fixed bed reactor at atmospheric pressure was studied over mn/na2wo4/sio2 catalyst. based on the properties of neural networks, an artificial neural network was used for model develo...