Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA filters are able to capture the inherent properties of textured images. The new filters are similar

Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA filters are able to capture the inherent properties of textured images. The new filters are similar

and the distances between images are emphasised by analysing the human response similarities with a multidimensional scaling technique called Curvilinear Component Analysis (CCA). On the other hand, we try to perform the same scene categorisation with a computational model based on an ICA filter description. 1.

. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximization network. In addition, the outputs of these filters are as independent as possible, since this infomax network performs Independent Components Analysis or ICA, for sparse (super-gaussian) component

clustering algorithms can be used to recover the ICA mixing matrix or its inverse, the ICA filters. It is well known that the independent components found by ICA form useful features for classification (Le et al., 2012; 2011; 2010), hence the connection between K-mean and ICA explains the empirical success

information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel

achieved by restricting to small windows, violating the translation-invariant nature of many real-world signals, and producing blocking artifacts in applications. Here, we reformulate the ICA problem for transformations computed through convolution with a bank of filters, and develop a generalization

In the square linear blind source separation problem, one must find a linear unmixing operator which can detangle the result x i (t) of mixing n unknown independent sources s i (t) through an unknown n \Theta n mixing matrix A(t) of causal linear filters: x i = P j a ij s j . We cast

We studied the performance of a double-spatial filtering method for classification of single-trial electroencephalog-raphy (EEG) data that couples the spherical surface Lapla-cian (SL) and independent component analysis (ICA). This method was evaluated in the context of a binary classifica-tion

presented Gabor convolve method and its comparison with ICA based techniques for Palm print recognition. We have taken four palms of a person from Singapore polytechnic database, two palms for training and two palms for testing. Palm cropping is old technique so we are not using palm crop technique