There exists a lot of redundancy in 3D meshes that can be exploited by Blind Source Separation (BSS) and Independent Component Analysis (ICA) for the goal of mesh compression. The 3D meshes geometry is spatially correlated on each direction in the Cartesian coordinate system. In the context of mesh compression with BSS algorithm, it is proposed to take the correlated geometry of 3D mesh as obse...

This contribution describes the monitoring on a pilot-scale sequencing batch reactor (SBR) using a batchwise multiway independent component analysis method (MICA) which can extract meaningful hidden information from non-Gaussian data. Given that independent component analysis (ICA) is superior to principal component analysis (PCA) to extract features from non-Gaussian data sets, the use of ICA ...

Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). A limitation of the standard fMRI ICA model is that a given component's time course is required to have the same delay at every voxel. As spatially varying delays (SVDs) may be...

The objective of this study is to combine multiple images of a scene acquired by different sensors to create a new image with all important information from the input images. Recent studies show that bases trained using Independent Component Analysis (ICA) is effective in multisensor fusion and has improved performance over traditional wavelet approaches. In the ICA based fusion, the coefficien...

In this paper, independent component analysis (ICA) is used for blind source separation of biomedical signals. Visual and quantitative tests of the ability of ICA to separate signals were performed using a fast ICA algorithm. Results obtained from simulated and FECG signals show that the ICA performance using the whitening matrix of the mixed signals was superior to that of random initial weights.

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...

Multispectral approach to brain MRI analysis has shown great advance recently in pathology and tissue analysis. However, poor performance of the feature extraction and classification techniques involved in it discourages radiologists to use it in clinical applications. Transform based feature extraction methods like Independent Component Analysis (ICA) and its variants have contributed a lot in...

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 study presents a modified infomax model of Independent Component Analysis (ICA) for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extra...

We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We t...