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More recently, some researchers advocated for the computation of optimal weights on individual networks for a group of labels, and achieved better performance than the methods operating on single labels.
Experimental results of different methods operating on PDF and SWF data are illustrated in Figs. 15 and 16, respectively.
Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising.
The direct and indirect methods are further classified as spatial [4, 16] (which directly operate on the pixels) and frequency domain methods (operating on the image transforms [11, 16 18]).
Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes.
Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty.
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In this work, we define the input views that a DIBR method operates on as original views and output views it produces as the virtual (i.e. rendered) views.
Because activity measured by this method operates on a specific feature of the environment and yields a programmed consequence, we refer to it as operant activity.
Another category of methods operates on the whole 3D facial surface.
Region-level methods operate on a set of pixels and commonly rely on textural information for shadow detection [29, 30].
In this case, the methods operate on more local constraints trying to ensure, for example, that there are no abrupt changes in the timbre of two adjacent segments.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com