Search results
Results from the WOW.Com Content Network
The Photoshop and illusions.hu flavors also produce the same result when the top layer is pure white (the differences between these two are in how one interpolates between these 3 results). These three results coincide with gamma correction of the bottom layer with γ=2 (for top black), unchanged bottom layer (or, what is the same, γ=1; for ...
For temporal smoothing in real-time situations, one can instead use the temporal kernel referred to as the time-causal limit kernel, [8] which possesses similar properties in a time-causal situation (non-creation of new structures towards increasing scale and temporal scale covariance) as the Gaussian kernel obeys in the non-causal case. The ...
With the introduction of Illustrator 7 in 1997, Adobe made critical changes in the user interface with regard to path editing (and also to converge on the same user interface as Adobe Photoshop), and many users opted not to upgrade. Illustrator also began to support TrueType, effectively ending the "font wars" between PostScript Type 1 and ...
Within Adobe Photoshop's Image Size dialog, the image editor allows the user to manipulate both pixel dimensions and the size of the image on the printed document. These parameters work together to produce a printed image of the desired size and quality.
An illustrator is an artist who specializes in enhancing writing or concepts by providing a visual representation that corresponds to the content of the associated text or idea. The illustration may be intended to clarify complicated concepts or objects that are difficult to describe textually, which is the reason illustrations are often found ...
A kernel smoother is a statistical technique to estimate a real valued function: as the weighted average of neighboring observed data. The weight is defined by the kernel, such that closer points are given higher weights.
Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.
Filtering is causal but smoothing is batch processing of the same problem, namely, estimation of a time-series process based on serial incremental observations. But the usual and more common smoothing and filtering (in the sense of 1.) do not have such distinction because there is no distinction between hidden and observable.