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This feature-based approach is often more robust than the template-based approach described below. As such, it has become the state-of-the-art method for template matching, as it can match templates with non-rigid and out-of-plane transformations, as well as high background clutter and illumination changes. [7] [8] [9]
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
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.). In these cases, we have knowledge of the shape and aim to ...
Template matching theory describes the most basic approach to human pattern recognition. It is a theory that assumes every perceived object is stored as a "template" into long-term memory. [ 4 ] Incoming information is compared to these templates to find an exact match. [ 5 ]
Tree patterns are used in some programming languages as a general tool to process data based on its structure, e.g. C#, [1] F#, [2] Haskell, [3] Java [4], ML, Python, [5] Ruby, [6] Rust, [7] Scala, [8] Swift [9] and the symbolic mathematics language Mathematica have special syntax for expressing tree patterns and a language construct for ...
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.
The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects.
On the technical side, the matched filter is a weighted least-squares method based on the (heteroscedastic) frequency-domain data (where the "weights" are determined via the noise spectrum, see also previous section), or equivalently, a least-squares method applied to the whitened data.