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This logo image consists only of simple geometric shapes or text. It does not meet the threshold of originality needed for copyright protection, and is therefore in the public domain. Although it is free of copyright restrictions, this image may still be subject to other restrictions.
Although it is free of copyright restrictions, this image may still be subject to other restrictions. See WP:PD § Fonts and typefaces or Template talk:PD-textlogo for more information. This work includes material that may be protected as a trademark in some jurisdictions.
Instead, you can list the image without actually displaying it. For example, let's say this image was a fair use image: If it were a fair use image (it isn't, it is in the public domain as a work of the U.S. government), then displaying it on one's userspace (or here in the Wikipedia project space) would be against policy. So, alternatively one ...
Template matching with rotated templates. For templates without strong features, or for when the bulk of a template image constitutes the matching image as a whole, a template-based approach may be effective. Since template-based matching may require sampling of a large number of data points, it is often desirable to reduce the number of ...
Thus, if the resulting enlarged image is recorded on contrasty photographic paper, the partial cancellation emphasizes the high-spatial-frequency information (fine detail) in the original, without loss of highlight or shadow detail. The resulting print appears more acute than one made without the unsharp mask: its acutance is increased.
The difference between a small and large Gaussian blur. In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.