The estimation of parametric global motion using non-linear optimization is a fundamental technique in computer vision. Such schemes are able to recover various motion models (translation, rotation, affine, projective) with subpixel accuracy. The parametric motion is computed using a first order Taylor expansions of the registered images. But, it is limited to the estimation of small motions, a...

Accurate estimation of latent heat flux (LE) is critical in characterizing semiarid ecosystems. Many LE algorithms have been developed during the past few decades. However, the algorithms have not been directly compared, particularly over global semiarid ecosystems. In this paper, we evaluated the performance of five LE models over semiarid ecosystems such as grassland, shrub, and savanna using...

Within the framework of Taylor Model implementations, the multiplication of Taylor models is the most time intensive operation, and thus benefits most from performance improvements. We note that in the mulplication, every coefficient is utilized repeatedly in various products, and so it is possible to do limited pre-processing of the coefficients without performance penalty due to the preproces...

The prediction properties of Aitken’s iterated ∆ process, Wynn’s epsilon algorithm, and Brezinski’s iterated theta algorithm for (formal) power series are analyzed. As a first step, the defining recursive schemes of these transformations are suitably rearranged in order to permit the derivation of accuracy-through-order relationships. On the basis of these relationships, the rational approximan...

This article considers transient, planar Poiseuille flows for viscoelastic fluids. We propose a novel time-dependent hybrid finite volume (fv)/finite element (fe) algorithm. This approach combines a Taylor-Galerkin fe-treatment for mass and momentum conservation equations, with a cell-vertex fv-discretisation of the hyperbolic stress constitutive equation. A consistent formulation for the const...

Curved Trajectories Algorithm (CTA) is a package for the minimization of unconstrained functions of several variables with intervals on the variables. The core algorithm is novel in that steps may follow polynomial space curves instead of straight lines. The space curves result from truncations of a Taylor series expansion of the Gradient inverse function. When the series is convergent and the ...

Gradient based motion estimation techniques (GM) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, a¢ne, projective). These methods estimate the motion between two images using a ...rst order Taylor expansion of the registered images. This paper...

Second order hold is a method that enables the discretization of input-driven nonlinear systems to be carried out with high precision. A new discretization scheme combining second order hold with the Taylor-series is proposed. The sampled-data representation and mathematical structure are explored. Both exact and approximate sampled-data representations are described in detail. The performance ...

This paper presents a Taylor series approach for solving linear fractional de-centralized bi-level multi-objective decision-making (LFDBL-MODM) problems with asingle decision maker at the upper level and multiple decision makers at the lower level.In the proposed approach, the membership functions associated with each objective(s) ofthe level(s) of LFDBL-MODM are transformed by using a Taylor s...

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