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If for example the pixel is located on a dark side of an edge it will most probably take the mean value of the dark region. On the other hand, should the pixel be on the lighter side of an edge it will most probably take a light value. On the event that the pixel is located on the edge it will take the value of the more smooth, least textured ...
In computer graphics, anti-aliasing improves the appearance of "jagged" polygon edges, or "jaggies", so they are smoothed out on the screen. However, it incurs a performance cost for the graphics card and uses more video memory. The level of anti-aliasing determines how smooth polygon edges are (and how much video memory it consumes).
The xbr family is very useful for creating smooth edges. It will however deform the shape significantly, which in many cases creates a very appealing result. However it will create an effect similar to posterization by grouping together local areas into a single colour. It will also remove small details if in-between larger ones which connect ...
Edge-preserving filters are designed to automatically limit the smoothing at “edges” in images measured, e.g., by high gradient magnitudes. For example, the motivation for anisotropic diffusion (also called nonuniform or variable conductance diffusion) is that a Gaussian smoothed image is a single time slice of the solution to the heat ...
Feathering is most commonly used on a paintbrush tool in computer graphics software. This form of feathering makes the painted area appear smooth. It may give the effect of an airbrush or spraypaint. Color is concentrated at the center of the brush area, and it blends out toward the edges.
This can also be thought of as how much contrast is added at the edges. It does not affect the width of the edge rims. Radius affects the size of the edges to be enhanced or how wide the edge rims become, so a smaller radius enhances smaller-scale detail. Higher radius values can cause halos at the edges, a detectable faint light rim around ...
Most edge-detection algorithms are sensitive to noise; the 2-D Laplacian filter, built from a discretization of the Laplace operator, is highly sensitive to noisy environments. Using a Gaussian Blur filter before edge detection aims to reduce the level of noise in the image, which improves the result of the following edge-detection algorithm.
As both edge and noise will be identified as a high frequency signal, a simple Gaussian filter will add a smooth effect on both of them. However, in order to reach high accuracy of detection of the real edge, it is expected that a more smooth effect should be applied to noise and a less smooth effect should be added to the edge.