The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform

Julien C. Richefeu - One of the best experts on this subject based on the ideXlab platform.

  • A new motion detection algorithm based on Σ-Δ background estimation
    Pattern Recognition Letters, 2007
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    International audienceMotion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose anew method based on a simple recursive non linear Operator, the Sigma-Delta filter. Used along with a spatiotemporal regularization algorithm,it allows robust, computationally efficient and accurate motion detection. To deal with complex scenes containing a wide range of motionmodels with very different time constants, we propose a generalization of the basic model to multiple Sigma-Delta estimation

  • A new motion detection algorithm based on Sigma-Delta background estimation
    Pattern Recognition Letters, 2007
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    Motion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose a new method based on a simple recursive non linear Operator, the Sigma-Delta filter. Used along with a spatiotemporal regularization algorithm, it allows robust, computationally efficient and accurate motion detection. To deal with complex scenes containing a wide range of motion models with very different time constants, we propose a generalization of the basic model to multiple Sigma-Delta estimation.

  • A robust and computationally efficient motion detection algorithm based on sigma-delta background estimation
    2004
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    This paper presents a new algorithm to detect moving objects within a scene acquired by a stationary camera. A simple recursive non linear Operator, the Sigma-Delta filter, is used to estimate two orders of temporal statistics for every pixel of the image. The output data provide a scene characterization allowing a simple and efficient pixel-level change detection framework. For a more suitable detection, exploiting spatial correlation in these data is necessary. We use them as a multiple observation field in a Markov model, leading to a spatiotemporal regularization of the pixel-level solution. This method yields a good trade-off in terms of robustness and accuracy, with a minimal cost in memory and a low computational complexity.

Antoine Manzanera - One of the best experts on this subject based on the ideXlab platform.

  • A new motion detection algorithm based on Σ-Δ background estimation
    Pattern Recognition Letters, 2007
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    International audienceMotion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose anew method based on a simple recursive non linear Operator, the Sigma-Delta filter. Used along with a spatiotemporal regularization algorithm,it allows robust, computationally efficient and accurate motion detection. To deal with complex scenes containing a wide range of motionmodels with very different time constants, we propose a generalization of the basic model to multiple Sigma-Delta estimation

  • A new motion detection algorithm based on Sigma-Delta background estimation
    Pattern Recognition Letters, 2007
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    Motion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose a new method based on a simple recursive non linear Operator, the Sigma-Delta filter. Used along with a spatiotemporal regularization algorithm, it allows robust, computationally efficient and accurate motion detection. To deal with complex scenes containing a wide range of motion models with very different time constants, we propose a generalization of the basic model to multiple Sigma-Delta estimation.

  • A robust and computationally efficient motion detection algorithm based on sigma-delta background estimation
    2004
    Co-Authors: Antoine Manzanera, Julien C. Richefeu
    Abstract:

    This paper presents a new algorithm to detect moving objects within a scene acquired by a stationary camera. A simple recursive non linear Operator, the Sigma-Delta filter, is used to estimate two orders of temporal statistics for every pixel of the image. The output data provide a scene characterization allowing a simple and efficient pixel-level change detection framework. For a more suitable detection, exploiting spatial correlation in these data is necessary. We use them as a multiple observation field in a Markov model, leading to a spatiotemporal regularization of the pixel-level solution. This method yields a good trade-off in terms of robustness and accuracy, with a minimal cost in memory and a low computational complexity.

Zahir Seyidmamedov - One of the best experts on this subject based on the ideXlab platform.

  • Determination of unknown elastoplastic properties in signorini problem
    International Journal of Non-Linear Mechanics, 1998
    Co-Authors: A. Hasanov, Zahir Seyidmamedov
    Abstract:

    Abstract The problem of recovering the plasticity function of Non-Linear Lame equation from the knowledge of penetration diagram is considered. Mathematical modelling of the identification problem leads to an inverse Signorini problem for a Non-Linear Operator with a non-local additional condition (measured data). Using a variational method coefficient stability in H1 is proved. Then based on this result, the existence of a quasisolution is obtained in a physically admissible class of coefficients. The numerical method and examples are also presented.

Michael J. Grimble - One of the best experts on this subject based on the ideXlab platform.

  • Non-Linear minimum variance state-space-based estimation for discrete-time multi-channel systems
    IET Signal Processing, 2011
    Co-Authors: Michael J. Grimble
    Abstract:

    A new state equation and Non-Linear Operator-based approach to estimation is introduced for discrete-time multi-channel systems. This is a type of deconvolution or inferential estimation problem, where a signal enters a communications channel involving both Non-Linearities and transport delays. The measurements are corrupted by a coloured noise signal, which is correlated with the signal to be estimated both at the inputs and outputs of the channel. The communications channel may include either static or dynamic Non-Linearities represented in a general Non-Linear Operator form. The optimal Non-Linear estimator is derived in terms of the state equations and Non-Linear Operators that describe the system. The algorithm is relatively simple to derive and to implement in the form of a recursive algorithm. The main advantage of the approach is the simplicity of the Non-Linear estimator theory and the straightforward structure of the resulting solution. The results may be applied to the solution of channel equalisation problems in communications or fault detection problems in control applications

  • Optimal minimum variance estimation for Non-Linear discrete-time multichannel systems
    IET Signal Processing, 2010
    Co-Authors: Michael J. Grimble, S. Ali Naz
    Abstract:

    A Non-Linear Operator approach to estimation in discrete-time multivariable systems is described. It involves inferential estimation of a signal which enters a communication channel that contains Non-Linearities and transport delays. The measurements are assumed to be corrupted by a coloured noise signal correlated with the signal to be estimated. The solution of the Non-Linear estimation problem is obtained using nonlinear Operators. The signal and noise channels may be grossly Non-Linear and are represented in a very general Non-Linear Operator form. The resulting so-called Wiener Non-Linear minimum variance estimation algorithm is relatively simple to implement. The optimal Non-Linear estimator is derived in terms of the nonlinear Operators and can be implemented as a recursive algorithm using a discrete-time Non-Linear difference equation. In the limiting case of a linear system, the estimator has the form of a Wiener filter in discrete-time polynomial matrix system form. A Non-Linear channel equalisation problem is considered for the design example

  • GMV control of Non-Linear continuous-time systems including common delays and state-space models
    International Journal of Control, 2007
    Co-Authors: Michael J. Grimble
    Abstract:

    A Non-Linear generalized minimum variance control law is proposed for the control of Non-Linear continuous-time multivariable systems with common delays on input and output channels. The quadratic cost index involves both error and control signal costing terms. The solution for the control law is obtained using a Non-Linear Operator representation of the plant and a linear state-equation model for the disturbance and reference models. The reference and disturbance models are represented by linear subsystems. However, the plant model can be in a very general Non-Linear Operator form, which could involve state-space, transfer Operators or Non-Linear function look up tables. The structure of the system and criterion is chosen so that a simple controller structure and solution is obtained. The controller obtained is simple to implement, particularly in one form, which might be considered to be a state-space version of a Non-Linear Smith predictor. The results are related to those for discrete-time systems but...

Vipin Kumar Singh - One of the best experts on this subject based on the ideXlab platform.