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A clipping path (or "deep etch" [1]) is a closed vector path, or shape, used to cut out a 2D image in image editing software. Anything inside the path will be included after the clipping path is applied; anything outside the path will be omitted from the output.
Feathering is not only used on paintbrushes in computer graphics software. Feathering may also blend the edges of a selected feature into the background of the image. When composing an image from pieces of other images, feathering helps make added features look "in place" with the background image.
This image was scaled up using nearest-neighbor interpolation.Thus, the "jaggies" on the edges of the symbols became more prominent. Jaggies are artifacts in raster images, most frequently from aliasing, [1] which in turn is often caused by non-linear mixing effects producing high-frequency components, or missing or poor anti-aliasing filtering prior to sampling.
Simulation showing how adjusting the angle of view of a camera, while varying the camera's distance and keeping the object in frame, results in vastly differing images. At narrow angles and long distances, light rays are nearly parallel, resulting in a "flattened" image. At wide angles and short distances, objects appear foreshortened or distorted.
Edges extracted from non-trivial images are often hampered by fragmentation, meaning that the edge curves are not connected, missing edge segments as well as false edges not corresponding to interesting phenomena in the image – thus complicating the subsequent task of interpreting the image data.
For artistic effect, vignetting is sometimes applied to an otherwise un-vignetted photograph and can be achieved by burning the outer edges of the photograph (with film stock) or using digital imaging techniques, such as masking darkened edges. The Lens Correction filter in Photoshop can also achieve the same effect.
Edge enhancement is an image processing filter that enhances the edge contrast of an image or video in an attempt to improve its acutance (apparent sharpness).. The filter works by identifying sharp edge boundaries in the image, such as the edge between a subject and a background of a contrasting color, and increasing the image contrast in the area immediately around the edge.
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.