https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Mukhopadhyay%20-%20Hough%20Transform%20-%202014.pdf

A survey of Hough Transform

Transverse momentum spectra of electrons (p(T)(e)) from semileptonic weak decays of heavy-flavor mesons in the range of 0.3 < p(T)(e) < 9.0 GeV/c have been measured at midrapidity (|y| < 0.35) by the PHENIX experiment at the Relativistic Heavy Ion Collider in p + p and Au + Au collisions at root s(NN) = 200 GeV. In addition, the azimuthal anisotropy parameter v(2) has been measured for 0.3 < p(T)(e) < 5.0 GeV/c in Au + Au collisions. The substantial modification in the p(T)(e) spectra in Au + Au compared with p + p collisions as well as the nonzero v(2) indicate substantial interactions and flow of heavy quarks in traversing the produced medium. Comparisons of these observables with detailed theoretical calculations can be used to identify the nature of these interactions and to quantify their extent.

Heavy-quark production in p + p and energy loss and flow of heavy quarks in Au + Au collisions at √sNN=200 GeV

Probabilistic and non-probabilistic Hough transforms: Overview and comparisons

Modification of hough transform for circles and ellipses detection using a 2-dimensional array

Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)

A combinatorial Hough transform

A new algorithm for the Hough transform is presented. It uses information available in the distribution of image points to calculate the parameters associated with combinations of the minimum number of points necessary to define an instance of the shape under detection. The method requires only one dimensional accumulation of evidence to determine the parameters associated with a given shape. Using the algorithm, the Hough transform of sparse images is more efficiently calculated. Dense images may be segmented and similarly processed. The method also provides a feedback mechanism between image and transform space whereby contiguity of feature points and endpoints of curves may be determined.

A Dynamic Combinatorial Hough Transform for Straight Lines and Circles.

Normally, in parallel implementations of the Hough transform either the transform space or the set of image features can be distributed among the processing elements. A method is proposed to link parallel access to feature points in the image, and parallel access to the transform space. A synchronous processing sequence is suggested such that both can be distributed. Real-time performance has been obtained on a MIMD distributed memory architecture.

Synchronous multiproccessor implementation of the Hough transform

The paper describes a new algorithm for the Hough transform, the combinatorial Hough transform, an improvement to the Hough space accumulator which gives greater resolution for a fixed size, and a new method for detecting line end points using the transform. These are described separately and implemented together to give a new algorithm with improved resolution. Also a method for the performance analysis of Hough transforms is introduced and exercised on the new algorithms, comparing their error performance with that of the standard Hough transform algorithm. It is found that the new algorithm with the parameter space improvements offers some real advantages over the standard and combinatorial algorithms, though this is dependent on the precise details of the implementation. The error analysis procedure is described in some detail and its effectiveness is discussed together with desirable enhancements.

http://ieeexplore.ieee.org/iel3/1226/3373/00114274.pdf

D. Ben-Tzvi

Papers

A combinatorial Hough transform

A Dynamic Combinatorial Hough Transform for Straight Lines and Circles.

Synchronous multiproccessor implementation of the Hough transform

Performance improvements to the Hough transform

Efficient parallel implementation of the Hough transform on a distributed memory system