An image processing approach for underdetermined blind separation of nonstationary sources

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An image processing approach for underdetermined blind separation of nonstationary sources

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dc.contributor.author Abed-Meraim, K
dc.contributor.author Linh-Trung, N
dc.contributor.author Sucic, V
dc.contributor.author Tupin, F
dc.contributor.author Boashash, B
dc.date.accessioned 2012-06-19T05:54:34Z
dc.date.available 2012-06-19T05:54:34Z
dc.date.issued 2003
dc.identifier.citation Paper: K. Abed-Meraim, N. Linh-Trung, V. Sucic, F. Tupin, and B. Boashash, "An image processing approach for underdetermined blind separation of nonstationary sources," in Proc. 3rd International Symposium on Image and Signal Processing and Analysis (ISPA 2003), Rome, Italy, September, 2003, vol. 1, pp. 347-352 en_US
dc.identifier.uri http://dx.doi.org/10.1109/ISPA.2003.1296921
dc.identifier.uri http://hdl.handle.net/10576/10861
dc.description.abstract This paper presents a new approach for blind separation of nonstationary frequency-modulated (FM) sources in the underdetermined case (i.e., more sources than sensors) using their time-frequency distributions (TFDs). The underlying idea of the proposed blind source separation (BSS) method is based on the observation that a monocomponent FM signal is represented by a linear feature corresponding to the 'energy concentration points' in the time-frequency (TF) image. Therefore, we propose to adapt an existing 'road network extraction' method [Tupin et al., (1998)] for the detection and separation of the source signal components from the spatially averaged TF image of their mixtures. The sources spatial signatures are then used to group together (classify) the components of the same source (or equivalently, the same spatial direction). Simulation examples are provided to assess the performance of the proposed algorithm in various scenarios. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title An image processing approach for underdetermined blind separation of nonstationary sources en_US
dc.type Article en_US

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