The Experts below are selected from a list of 93555 Experts worldwide ranked by ideXlab platform

S B Serpico - One of the best experts on this subject based on the ideXlab platform.

  • sar amplitude probability density function estimation based on a generalized gaussian scattering model
    Remote Sensing, 2004
    Co-Authors: Gabriele Moser, Josiane Zerubia, S B Serpico
    Abstract:

    In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on Synthetic Aperture Radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In the present paper, an innovative parametric estimation methodology for SAR amplitude data is proposed, that takes into account the physical nature of the scattering phenomena generating a SAR image by adopting a generalized Gaussian (GG) model for the backscattering phenomena. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed "method-of-log-cumulants" (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions, and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude probability density function better than several previously proposed parametric models for backscattering phenomena.

Mariya Sirotina - One of the best experts on this subject based on the ideXlab platform.

  • joint probability density function of modulated synchronous flow interval duration under conditions of fixed dead time
    International Conference on Information Technologies and Mathematical Modelling, 2015
    Co-Authors: Aleksandr Gortsev, Mariya Sirotina
    Abstract:

    A modulated synchronous doubly stochastic flow under conditions of a fixed dead time is considered. After each registered event there is a time of fixed duration T (dead time), during which another flow events are inaccessible for observation. When duration of the dead time period finishes, the first happened event creates the dead time period of duration T again and etc. An explicit form of a probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow under conditions of a fixed dead time is derived. Also an explicit form of a joint probability density function for modulated synchronous flow interval duration is obtained. A recurrent conditions for modulated synchronous flow as well as some probabilistic characteristics of the flow are obtained using the formula for a joint probability density function.

  • joint probability density function of modulated synchronous flow interval duration
    International Conference on Information Technologies and Mathematical Modelling, 2014
    Co-Authors: Aleksandr Gortsev, Mariya Sirotina
    Abstract:

    An explicit form of a probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow is derived. Also an explicit form of a joint probability density function for modulated synchronous flow interval duration is obtained. This flow is one of the mathematical models of information flows, which take place in digital networks with integral service. The flow is considered in stationary mode when there are no transition processes. A recurrent conditions for modulated synchronous flow are obtained using the formula for joint probability density function.

Venkatramanan Raman - One of the best experts on this subject based on the ideXlab platform.

  • A multienvironment conditional probability density function model for turbulent reacting flows
    Physics of Fluids, 2004
    Co-Authors: Venkatramanan Raman
    Abstract:

    The multienvironment conditional probability density function (MECPDF) model was first proposed by Fox [Computational Models for Turbulent Reacting Flows (Cambridge University Press, Cambridge, 2003)] as a simple extension of multienvironment probability density function models for turbulent reacting flows. Like the conditional moment closure (CMC) and the laminar flamelet model (LFM), the MECPDF model describes the reacting scalars conditioned on the value of the mixture fraction. However, unlike CMC and LFM, the new model provides a consistent description of conditional fluctuations in both the scalar dissipation rate and the reacting scalars, and hence can be used to model partial extinction and reignition in homogeneous turbulent reacting flows. In this work, a general derivation of the MECPDF model is presented for a single reaction-progress variable using the direct quadrature method of moments. Extensions of the model to multiple reaction-progress variables and conditioning on the mixture-fraction ...

Jose Munozperez - One of the best experts on this subject based on the ideXlab platform.

  • probability density function estimation with the frequency polygon transform
    Information Sciences, 2015
    Co-Authors: Ezequiel Lopezrubio, Jose Munozperez
    Abstract:

    A probability density function estimator is proposed, which is based on frequency polygons.Its convergence to the true density is formally proved.A mode finding algorithm is also proposed, as an alternative to mean-shift.Our approach outperforms histogram and kernel based estimators in synthetic and real datasets.Our proposal is shown to be suitable to object tracking in video sequences. Most current nonparametric approaches to probability density function estimation are based on the kernel density estimator, also known as the Parzen window estimator. A usual alternative is the multivariate histogram, which features a low computational complexity. Multivariate frequency polygons have often been neglected, even though they share many of the advantages of the histograms, while they are continuous unlike the histograms. Here we build on our previous work on histograms in order to propose a new probability density estimator which is based on averaging multivariate frequency polygons. The convergence of the estimator is formally proved. Experiments are carried out with synthetic and real machine learning datasets. Finally, image denoising and object tracking applications are also considered.

Gabriele Moser - One of the best experts on this subject based on the ideXlab platform.

  • sar amplitude probability density function estimation based on a generalized gaussian scattering model
    Remote Sensing, 2004
    Co-Authors: Gabriele Moser, Josiane Zerubia, S B Serpico
    Abstract:

    In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on Synthetic Aperture Radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In the present paper, an innovative parametric estimation methodology for SAR amplitude data is proposed, that takes into account the physical nature of the scattering phenomena generating a SAR image by adopting a generalized Gaussian (GG) model for the backscattering phenomena. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed "method-of-log-cumulants" (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions, and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude probability density function better than several previously proposed parametric models for backscattering phenomena.