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RANSAC can only estimate one model for a particular data set. As for any one-model approach when two (or more) model instances exist, RANSAC may fail to find either one. The Hough transform is one alternative robust estimation technique that may be useful when more than one model instance is present.
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" [3] after the related 1962 patent of Paul Hough. [ 4 ] [ 5 ] The transform was popularized in the computer vision community by Dana H. Ballard through a 1981 journal article titled " Generalizing ...
Hough transforms are techniques for object detection, a critical step in many implementations of computer vision, or data mining from images. Specifically, the Randomized Hough transform is a probabilistic variant to the classical Hough transform, and is commonly used to detect curves (straight line, circle, ellipse, etc.) [1] The basic idea of Hough transform (HT) is to implement a voting ...
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 ...
How variable rate caps work. In many cases, lenders set caps on variable-rate products. This was designed to protect consumer borrowers from the kind of runaway interest the country saw during the ...
Conventional Hough transform EBSD and HR-EBSD [84] [98] Conventional EBSD HR-EBSD Absolute orientation 2° N/A: Misorientation 0.1° to 0.5° 0.006° (1×10 −4 rad) GND @ 1 μm step In lines/m 2 (b = 0.3 nm) > 3×10 13 > 3×10 11: Relative residual strain N/A Deviatoric elastic strain 1×10 −4: Example tasks Texture, microstructure, etc ...
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The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix. It is a specialization of the Hough transform.