hough {PET} | R Documentation |
The function implements a Hough transformation for an image.
hough(oData, mode=1, XYSamples=nrow(oData), DeltaXY=1.0, XYmin=-0.5*DeltaXY*(XYSamples-1), ThetaSamples=181, RhoSamples=2*round(sqrt(sum((dim(oData))^2))/2)+1, ThetaMin=0, RhoMin=-0.5*((2*round(sqrt(sum((dim(oData))^2))/2)+1)-1), DeltaTheta=pi/ThetaSamples, DeltaRho=(2*abs(RhoMin)+1)/RhoSamples, DebugLevel = "Normal")
oData |
(matrix) A matrix that contains the image (for the Hough transformation). |
mode |
(integer) The optimization strategy for the Hough transformation. Default is |
XYSamples |
(integer) Specifies the number of samples on the x-axis (rows) and y-axis (columns) of |
DeltaXY |
(double) Specifies the sampling distance of both axes in the image. Defaults to |
XYmin |
(double) Specifies the minimum sample position in the image on the first and second axis. If not given, the image is centered around the middle. Defaults to |
ThetaSamples |
(integer) Specifies the number of samples in the angular parameter theta in the Hough transformation image. It is sampled linearly from 0 to (approximately) pi radians. Defaults to |
RhoSamples |
Specifies the number of samples in the distance parameter rho in the Hough transformation image. |
ThetaMin |
(double) Specifies the minimum sample position in the Hough transformation image on the first axis. Defaults to |
RhoMin |
(double) Specifies the minimum sample position in the Hough transformation image on the second axis. |
DeltaTheta |
(double) Angular sampling distance. Defaults to |
DeltaRho |
(double) Specifies the sampling distance in rho. The program will center the sampling points around 0. Defaults to |
DebugLevel |
(character) This parameter controls the level of output. Defaults to |
It is shown in the paper of P.Toft that the Hough transformation can be defined in a way that gives exactly the same discrete parameter domain as found with the nearest neighbour approximation of the discrete Radon transformation. The Hough transformation does not have the same property. It is also shown that the Hough transformation behaves very differently when changing the sampling intervals in the discrete parameter domain, compared to the discrete Radon transformation. Furthermore different optimization schemes for the Hough transformation are described there.
hData |
A matrix, that contains the Hough transformation of |
Header |
A list of following values:
|
call |
Arguments of the call to |
Peter Toft, Joern Schulz jschulz78@web.de.
Toft, Peter, Ph.D. Thesis, The Radon Transform - Theory and Implementation, Department of Mathematical Modelling Section for Digital Signal Processing, Technical University of Denmark, 1996.
http://eivind.imm.dtu.dk/staff/ptoft/ptoft_papers.html
P <- phantom() hP <- hough(P) viewData(list(P, hP$hData), list("Phantom", "Hough transformed phantom")) rm(P,hP)