147 research outputs found

    On the conditions for valid objective functions in blind separation of independent and dependent sources

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    It is well known that independent sources can be blindly detected and separated, one by one, from linear mixtures by identifying local extrema of certain objective functions (contrasts), like negentropy, Non-Gaussianity measures, kurtosis, etc. It was also suggested in [1], and verified in practice in [2,4], that some of these measures remain useful for particular cases with dependent sources, but not much work has been done in this respect and a rigorous theoretical ground still lacks. In this paper, it is shown that, if a specific type of pairwise dependence among sources exists, called Linear Conditional Expectation (LCE) law, then a family of objective functions are valid for their separation. Interestingly, this particular type of dependence arises in modeling material abundances in the spectral unmixing problem of remote sensed images. In this work, a theoretical novel approach is used to analyze Shannon entropy (SE), Non-Gaussianity (NG) measure and absolute moments of arbitrarily order, i.e. Generic Absolute (GA) moments for the separation of sources allowing them to be dependent. We provide theoretical results that show the conditions under which sources are isolated by searching for a maximum or a minimum. Also, simple and efficient algorithms based on Parzen windows estimations of probability density functions (pdfs) and Newton-Raphson iterations are proposed for the separation of dependent or independent sources. A set of simulation results on synthetic data and an application to the blind spectral unmixing problem are provided in order to validate our theoretical results and compare these algorithms against FastICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis algorithm (BCA). It is shown that, for dependent sources verifying the LCE law, the NG measure provides the best separation results.Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto Argentino de Radioastronomia (i); Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentin

    Hyperspectral colon tissue cell classification

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    A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

    Using generic order moments for separation of dependent sources with linear conditional expectations

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    In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia21ª European Signal Processing ConferenceMarrakechMarruecosEuropean Signal Processing Society (EURASIP

    Does independent component analysis play a role in unmixing hyperspectral data?

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    Tópico en Procesamiento de Señales: Separación Ciega de Fuentes y Aplicaciones

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    En este artículo presentamos el problema de la Separación Ciega de Fuentes ("Blind Source Separation - BSS"), un tópico de gran interés en el área del Procesamiento de Señales con aplicaciones al procesamiento de imágenes satelitales y a la separación de fuentes de radiación en Radioastronomía entre otras.Los algoritmos de BSS (?Blind Source Separation - BSS") han sido desarrollados ampliamente durante los últimos 15 años y han sido aplicados con éxito a problemas prácticos en diversas áreas científicas y tecnológicas, a saber: separación de señales acústicas (audio), análisis de señales neuronales en Neurociencias, etc. (Comon & Jutten, 2010). Además, BSS también representa una herramienta muy útil para el procesamiento de imágenes satelitales y en Radioastronomía, dos aplicaciones que abordamos en este artículo.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentin

    Convolutive Blind Source Separation Methods

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    In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks

    Multimodal methods for blind source separation of audio sources

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    The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.
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