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After drawing the outline, back-face culling is set back to normal to draw the shading and optional textures of the object. Finally, the image is composited via Z-buffering, as the back-faces always lie deeper in the scene than the front-faces. The result is that the object is drawn with a black outline and interior contour lines.
An "image" in the form of a table is much more convenient to edit than an uploaded image. If all the cells in a row are empty the cells still show up. If the header cell is also empty for that row all the cells show up, but they are narrow. That can be fixed with a simple <br> in one of the cells. That is what is done here:
A mesh need not be simplicial because an arbitrary subset of nodes of a cell is not necessarily a cell: e.g., three nodes of a quad does not define a cell. However, two cells intersect at cells: e.g. a quad does not have a node in its interior. The intersection of two cells may be several cells: e.g., two quads may share two edges.
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 objects. Fine detail needs a smaller radius. Radius and amount interact; reducing one allows more of the other.
in which each edge points to two vertices, two faces, and the four (clockwise and counterclockwise) edges that touch them. Winged-edge meshes allow constant time traversal of the surface, but with higher storage requirements. Half-edge meshes Similar to winged-edge meshes except that only half the edge traversal information is used. (see OpenMesh)
The result therefore shows how "abruptly" or "smoothly" the image changes at that point, and therefore how likely it is that part of the image represents an edge, as well as how that edge is likely to be oriented. In practice, the magnitude (likelihood of an edge) calculation is more reliable and easier to interpret than the direction calculation.
Left: original image. Right: image processed with bilateral filter. A bilateral filter is a non-linear, edge-preserving, and noise-reducing smoothing filter for images. It replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution.