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The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
PatchMatch is an algorithm used to quickly find correspondences (or matches) between small square regions (or patches) of an image. It has various applications in image editing, such as reshuffling or removing objects from images or altering their aspect ratios without cropping or noticeably stretching them
Merge Structured comparison [b] Manual compare alignment Image compare Beyond Compare: Yes Yes Yes Yes Yes (Files and Folders) Yes (Pro only) Yes Yes Compare++: Yes Yes Yes Yes Yes (C/C++,C#,Java,Javascript,CSS3) diff: No Yes partly No No No diff3: No No Yes (non-optional) Eclipse (compare) Yes No (only ancestor) Yes No Ediff: Yes Yes Yes Yes ...
Via Photoshop: contact sheet, merge to HDR, PDF presentation, photomerge, Web picture gallery Proprietary: Adobe Lightroom: Library organization, RAW and JPEG editing, slideshow tools, print layout and preferences, web gallery creation and uploading Proprietary: Aperture: Proprietary: CDisplay
Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [2] navigation of mobile robots, [3] or edge detection in images.
Manual merging is also required when automatic merging runs into a change conflict; for instance, very few automatic merge tools can merge two changes to the same line of code (say, one that changes a function name, and another that adds a comment). In these cases, revision control systems resort to the user to specify the intended merge result.
A Block Matching Algorithm is a way of locating matching macroblocks in a sequence of digital video frames for the purposes of motion estimation.The underlying supposition behind motion estimation is that the patterns corresponding to objects and background in a frame of video sequence move within the frame to form corresponding objects on the subsequent frame.
Next, the rotated image is created with a nearest-neighbor scaling and rotation algorithm that simultaneously shrinks the big image back to its original size and rotates the image. Finally, overlooked single-pixel details are (optionally) restored if the corresponding pixel in the source image is different and the destination pixel has three ...