The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform

Deng Zili - One of the best experts on this subject based on the ideXlab platform.

Bai Jian Tang - One of the best experts on this subject based on the ideXlab platform.

Alexander G. Ramm - One of the best experts on this subject based on the ideXlab platform.

Antonio Vicino - One of the best experts on this subject based on the ideXlab platform.

  • optimal Estimation Theory for dynamic systems with set membership uncertainty an overview
    Automatica, 1991
    Co-Authors: M Milanese, Antonio Vicino
    Abstract:

    In many problems such as linear and nonlinear regressions, parameter and state Estimation of dynamic systems, state-space and time series prediction, interpolation, smoothing and functions approximation, one has to evaluate some unknown variable using available data. The data are always associated with some uncertainty and it is necessary to evaluate how this uncertainty affects the estimated variables. Typically, the problem is approached assuming a probabilistic description of uncertainty and applying statistical Estimation Theory. An interesting alternative approach, referred to as set membership or unknown but bounded (UBB) error description, has been investigated since the late 1960s. In this approach, uncertainty is described by an additive noise which is known only to have given integral (typically l2 of l1) or componentwise (l∞) bounds. In this paper we review the main results of this Theory, with special attention to the most recent advances obtained in the case of componentwise bounds.

Frank P. Ferrie - One of the best experts on this subject based on the ideXlab platform.

  • CVPR - Edge localization in surface reconstruction using optimal Estimation Theory
    Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1
    Co-Authors: S. Mathur, Frank P. Ferrie
    Abstract:

    Many relaxation based smoothing methods used in surface reconstruction algorithms filter out the effect of noise in image data, but result in the elimination of important discontinuity information as well. In this paper the inter-pixel interaction during relaxation is shown to be equivalent to a multiple measurement fusion problem which can be solved using optimal Estimation Theory. Pixels in a given neighbourhood act as noisy information sources, combining their information to update the state of that neighbourhood. By formulating discontinuities as another "noise" source in the image, and by using the so-called Curvature Consistency reconstruction algorithm on range images, it is shown that optimal Estimation Theory offers a method for the automatic and adaptive localization of discontinuities while providing a smooth piece wise continuous surface description.