Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of example shapes. However, PCA has the restriction that the input data must be drawn from a Gaussian distribution and is only able to describe global shape variations. In this paper we evaluate the use of an alternative shape decomposition, Independent ...

Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of features extracted by ICA (ICA features) has not been verified yet. As one of the reasons, it is considered that ICA features are obtained by increasing their independence rather than by increasing their class sep...

With an increasing number of security threats in recent years, the field automatic facial recognition has seen many new developments. The introduction face algorithms focuses on accuracy rate system. This paper introduces a system using Independent Component Analysis (lCA) for feature extraction and Support Vector Neural Network (SVNN) classification. As well as introducing comparison between S...

We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using independent component analysis (ICA). Our model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than brain sources. We discuss the properties of each of the s...

We propose the kernel-based nonlinear independent component analysis (ICA) method, which consists of two separate steps. First, we map the data to a high-dimensional feature space and perform dimension reduction to extract the effective subspace, which was achieved by kernel principal component analysis (PCA) and can be considered as a pre-processing step. Second, we need to adjust a linear tra...

The assumption that the problem of ambiguity in text analysis can only be solved if statistical dependencies of higher than second order are considered leads us to independent component analysis (ICA), a statistical formalism that takes higher-order dependencies into account. By assuming independence, ICA is capable of detecting a set of hidden vectors if only different linear mixtures of these...

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implemen...

A kind of image digital watermarking scheme is proposed in this paper. The scheme is based on Fast Independent Component Analysis (Fast ICA) and Discrete Wavelet Transform (DWT). In this scheme, a binary image is embedded into a wavelet approach sub-image. When extracting the watermarking, Fast ICA method is used. The experiment results show that the scheme is robust to many attacks. Keyword— B...

Image separation is defined as decomposing a real world image mixture into individual images objects. Independent component analysis is an active area of research and is being utilized for its capability in statistically independent separation images. Neural network algorithm ICA has been used to extract interference and mixed images and a very rapid developed statistical method during last few...

This paper addresses new face recognition method based on Independent Component Analysis(1CA) and Gabor filter. Our method consists of three parts. The first part is Gabor filtering on predefined fiducial points that could represent robust facial features from original face image. The second part is transforming the facial features into the basis space of ICA, which is able to represent individ...