We propose a method for fast face localisation and verification (identification) based on a robust form of correlation. Geometric and photometric normalisation of face images is achieved by direct minimisation. During optimisation, the correlation is estimated from a set of samples drawn from a Sobol sequence. This Monte-Carlo technique speeds the evaluation of correlation approximately twenty ...

Traditional rejection/reduction tests used in branch-and-prune methods for nonlinear systems usually are based on various forms of the interval Newton operator and constraint propagation techniques. Hence, they are relatively costly. This paper considers an additional phase of a branchand-prune method for the exclusion of regions not containing any solutions. Low-discrepancy sequences of Sobol ...

Quasi-Monte Carlo (QMC) points are a substitute for plain Monte (MC) that greatly improve integration accuracy under mild assumptions on the problem. Because QMC can give errors o(1/n) as $$n\rightarrow \infty $$ , and randomized versions attain root mean squared o(1/n), changing even one point change estimate by an amount much larger than error would have been worsen convergence rate. As resul...

Sensitivity analysis aims to characterize factors (i.e., model inputs) accounting for the amount of uncertainty in model output. Input factors are usually assumed to be independent, which may lead to incorrect conclusions. In this study, a combined sensitivity analysis approach, composed of the Sobol’ and Importance Measurement (IM) methods, is applied on a pesticide environmental risk indicato...

Sparse grid interpolation is widely used to provide good approximations to smooth functions in high dimensions based on relatively few function evaluations. By using an efficient conversion from the interpolating polynomial provided by evaluations on a sparse grid to a representation in terms of orthogonal polynomials (gPC representation), we show how to use these relatively few function evalua...

The variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing m...