8,096 research outputs found
Separating a Real-Life Nonlinear Image Mixture
When acquiring an image of a paper document, the image printed on the back page sometimes shows through. The mixture of the front- and back-page images thus obtained is markedly nonlinear, and thus constitutes a good real-life test case for nonlinear blind source separation.
This paper addresses a difficult version of this problem, corresponding to the use of "onion skin" paper, which results in a relatively strong nonlinearity of the mixture, which becomes close to singular in the lighter regions of the images. The separation is achieved through the MISEP technique, which is an extension of the well known INFOMAX method. The separation results are assessed with objective quality measures. They show an improvement over the results obtained with linear separation, but have room for further improvement
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
Blind image separation based on exponentiated transmuted Weibull distribution
In recent years the processing of blind image separation has been
investigated. As a result, a number of feature extraction algorithms for direct
application of such image structures have been developed. For example,
separation of mixed fingerprints found in any crime scene, in which a mixture
of two or more fingerprints may be obtained, for identification, we have to
separate them. In this paper, we have proposed a new technique for separating a
multiple mixed images based on exponentiated transmuted Weibull distribution.
To adaptively estimate the parameters of such score functions, an efficient
method based on maximum likelihood and genetic algorithm will be used. We also
calculate the accuracy of this proposed distribution and compare the
algorithmic performance using the efficient approach with other previous
generalized distributions. We find from the numerical results that the proposed
distribution has flexibility and an efficient resultComment: 14 pages, 12 figures, 4 tables. International Journal of Computer
Science and Information Security (IJCSIS),Vol. 14, No. 3, March 2016 (pp.
423-433
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