Search results
Results from the WOW.Com Content Network
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.).
Dana Harry Ballard (1946–2022) was a professor of computer science at the University of Texas at Austin and formerly with the University of Rochester. [1] Ballard attended MIT and graduated in 1967 with his bachelor's degree in aeronautics and astronautics. He then attended the University of Michigan for his masters in information and control ...
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 ...
Plot of the generalized hypergeometric function pFq(a b z) with a=(2,4,6,8) and b=(2,3,5,7,11) in the complex plane from -2-2i to 2+2i created with Mathematica 13.1 function ComplexPlot3D. In mathematics, a generalized hypergeometric series is a power series in which the ratio of successive coefficients indexed by n is a rational function of n.
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 ...
There are many other formulas giving the hypergeometric function as an algebraic number at special rational values of the parameters, some of which are listed in Gessel & Stanton (1982) and Koepf (1995). Some typical examples are given by
Open an Excel sheet with your historical sales data. Select data in the two columns with the date and net revenue data. Click on the Data tab and pick "Forecast Sheet."
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. When clusters of features are found to vote for the same pose of an object, the probability of the interpretation being correct is much higher than for any single feature.