The Hough transform ( /ˈhʌf/) is a feature extraction technique used in image analysis, computer vision, and digital image processing. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform.
The classical Hough transform was concerned with the identification of lines in the image, but later the Hough transform has been extended to identifying positions of arbitrary shapes, most commonly circles or ellipses. The Hough transform as it is universally used today was invented by Richard Duda and Peter Hart in 1972, who called it a "generalized Hough transform" after the related 1962 patent of Paul Hough. The transform was popularized in the computer vision community by Dana H. Ballard through a 1981 journal article titled "Generalizing the Hough transform to detect arbitrary shapes".
Other articles related to "hough transform, transform, hough":
... Hough Transform is used to cluster reliable model hypotheses to search for keys that agree upon a particular model pose ... Hough transform identifies clusters of features with a consistent interpretation by using each feature to vote for all object poses that are consistent with the feature ... The similarity transform implied by these 4 parameters is only an approximation to the full 6 degree-of-freedom pose space for a 3D object and also does not account for any non-rigid ...
... It was initially invented for machine analysis of bubble chamber photographs (Hough, 1959) ... The Hough transform was patented as U.S ... lines, which awkwardly leads to an unbounded transform space since the slope can go to infinity ...
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