Search results
Results From The WOW.Com Content Network
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.
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be a match." The patterns generally have the form of either sequences or tree structures.
This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors.
The generalized Hough transform (GHT), introduced by Dana H. Ballard in 1981, is the modification of the Hough transform using the principle of template matching. [1] The Hough transform was initially developed to detect analytically defined shapes (e.g., line, circle, ellipse etc.).
Pattern matching programming languages (2 C, 30 P) R. Regular expressions (1 C, 12 P) S. String matching algorithms (1 C, 16 P) Pages in category "Pattern matching"
Matrix matching involves comparing an image to a stored glyph on a pixel-by-pixel basis; it is also known as pattern matching, pattern recognition, or image correlation. This relies on the input glyph being correctly isolated from the rest of the image, and the stored glyph being in a similar font and at the same scale.
SIFT feature matching can be used in image stitching for fully automated panorama reconstruction from non-panoramic images. The SIFT features extracted from the input images are matched against each other to find k nearest-neighbors for each feature. These correspondences are then used to find m candidate matching images for each image.
Elastic matching is one of the pattern recognition techniques in computer science. Elastic matching (EM) is also known as deformable template, flexible matching, or nonlinear template matching. [1] Elastic matching can be defined as an optimization problem of two-dimensional warping specifying corresponding pixels between subjected images.