#### Filter Results:

- Full text PDF available (39)

#### Publication Year

1998

2017

- This year (2)
- Last 5 years (18)
- Last 10 years (43)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Ines Meganem, Philippe Deliot, Xavier Briottet, Yannick Deville, Shahram Hosseini
- IEEE Trans. Geoscience and Remote Sensing
- 2014

In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range [0.4-2.5 μm], rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at ground level to be uniformly irradiated and the scene to be… (More)

- Shahram Hosseini, Yannick Deville
- ICA
- 2004

We proposed recently a new method for separating linearquadratic mixtures of independent real sources, based on parametric identification of a recurrent separating structure using an ad hoc algorithm. In this paper, we develop a maximum likelihood approach providing an asymptotically efficient estimation of the model parameters. A major advantage of this… (More)

- Shahram Hosseini, Christian Jutten, Dinh-Tuan Pham
- IEEE Trans. Signal Processing
- 2003

A maximum likelihood (ML) approach is used to separate the instantaneous mixtures of temporally correlated, independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources. A Markov model is used to represent the joint probability density of successive samples of each source. The joint… (More)

- Christian Jutten, Massoud Babaie-Zadeh, Shahram Hosseini
- Signal Processing
- 2004

In this paper, we consider the nonlinear Blind Source Separation BSS and independent component analysis (ICA) problems, and especially uniqueness issues, presenting some new results. A fundamental di6culty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they are nonunique without a suitable regularization. In this paper,… (More)

- Johan Thomas, Yannick Deville, Shahram Hosseini
- IEEE Signal Processing Letters
- 2006

This letter presents new blind separation methods for moving average (MA) convolutive mixtures of independent MA processes. They consist of time-domain extensions of the FastICA algorithms developed by Hyvarinen and Oja for instantaneous mixtures. They perform a convolutive sphering in order to use parameter-free fast fixed-point algorithms associated with… (More)

- Shahram Hosseini, Yannick Deville
- IWANN
- 2003

In this paper, we propose an approach for separating linearquadratic mixtures of independent real sources. The method is based on parametric identi cation of a recurrent separating structure by means of an adaptive algorithm which uses the higher-order statistics of the outputs of this structure. We study the local stability of the recurrent structure and… (More)

- Zbynek Koldovský, Jirí Málek, Petr Tichavský, Yannick Deville, Shahram Hosseini
- Signal Processing
- 2009

We address independent component analysis (ICA) of piecewise stationary and nonGaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular nonGaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to… (More)

A quasi-maximum likelihood approach is used for separating the instantaneous mixtures of temporally correlated, independent sources without either any preliminary transformation or a priori assumption about the probability distribution of the sources. A first order Markov model is used to represent the joint probability density of successive samples of each… (More)

- Zbynek Koldovský, Jirí Málek, Petr Tichavský, Yannick Deville, Shahram Hosseini
- 2008 IEEE International Conference on Acoustics…
- 2008

We propose an extension of EFICA algorithm for piecewise stationary and non Gaussian signals. The proposed method is able to profit from varying distribution of the original signals and also from their varying variance, which is demonstrated by simulations with real-world signals. We show that in case of constant-variance signals, the accuracy of the method… (More)

- Yannick Deville, Shahram Hosseini
- 2007 9th International Symposium on Signal…
- 2007

This paper concerns blind mixture identification (BMI) and blind source separation (BSS). We consider non-stationary stochastic sources, more specifically sources with slight time-domain sparsity. We first propose a correlation-based BMI/BSS method for Linear-Quadratic mixtures, called LQ-TEMPCORR. We also investigate the applicability of this type of… (More)