We present a method to deal with adaptive noise cancelling based on independent component analysis (ICA). Although popular least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed ICA-based algorithm can utilize higher-order statistics. Additionally, extending to transform-domain adaptive filtering (TDAF) methods, normalized ICA-based algorithm...

CT image De-noising is an important research topic both in image processing and biomedical engineering. Independent component analysis (ICA) is a statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables. The curvelet transform as a multiscale transform has directional parameters occurs at all scale...

We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a transform that makes the data components well fit by a tree-structured graphical model. Treating the problem as a semiparametric statistical problem, we show that the optimal transform is found by minimizing a contrast fun...

In recent years, access to multimedia data has become much easier due to rapid growth of the internet. While this is usually considered an improvement of everyday life, it also makes unauthorized copying and distributing of multimedia data much easier, therefore presenting a field of watermarking. Many literatures have reported about Discrete Wavelet Transform watermarking techniques for data s...

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...

We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a transform that makes the data components well fit by a tree-structured graphical model. This tree-dependent component analysis (TCA) provides a tractable and flexible approach to weakening the assumption of independence in...

functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

We propose a new method for the blind robust watermarking of digital images based on independent component analysis (ICA). We apply ICA to compute some statistically independent transform coefficients where we embed the watermark. The main advantages of this approach are twofold. On the one hand, each user can define its own ICA-based transformation. These transformations behave as ‘‘private-ke...

Independent component analysis (ICA) is a new effective technique for separation of statistically independent sources existing simultaneously in observations. Generally, ICA requires that the number of sensors should be no less than the number of independent sources to ensure enough information for separation of all sources. In some practical applications, this requirement of ICA is not met and...

Independent Component Analysis (ICA) and Blind Source Separation (BSS) have become standard tools in multivariate data analysis. ICA continues to generate a flurry of research interest, resulting in increasing numbers of papers submitted to conferences and journals. Furthermore, there are many workshops and special sessions conducted in major conferences that focus on recent research results. T...