Spectral methods are global methods.
Because such learning methods are global and parallel search, they have more chance to converge toward global solution.
However, these stabilization methods are global motion compensation-based methods for perspective imaging, which cannot be used to correct the deformations in panoramic images for the following reasons.
First, spectral methods are global, i.e. they use the complete signal for computation, not just local extremes, similar sub-sequences or areas near the missing data.
As these methods are global optimizers, there is less possibility that they will fall in a local minima and are suitable for the selection of the parameters of the antecedent part.
The methods are: global thresholding, adaptive thresholding, and double adaptive thresholding.
Spatial autocorrelation statistics, such as Moran's I and Geary's C methods, are global in the sense that they estimate the overall degree of spatial autocorrelation in a dataset.
The new time-integration method is global, high order accurate, unconditionally stable and together with the approximation in space, it generates optimally sharp fully discrete energy estimates.
Since the only additional information available to the diffusion method is global network information, i.e. information not available in matches in the local neighborhood, we conclude that the global topology of the network provides important annotation information and this information is tapped into by the diffusion mechanism embodied in the cost function (eq. 1).
Our method is global, starting from constructing building blocks to the assembly of pathways.
