问题
Does anyone know the particular algorithm for Probabilistic Hough Transform in the OpenCV's implementation? I mean, is there a reference paper or documentation about the algorithm?
To get the idea, I can certainly look into the source code, but I wonder if there is any documentation about it. -- it's not in the source code's comments (OpenCV 1.0).
Thank you!
-Jin
回答1:
Here's an article about the Randomized Hough Transform which i believe to be the same as the "probabilistic Hough transform" used in OpenCV http://en.wikipedia.org/wiki/Randomized_Hough_Transform
basically, you dont fill up the accumulator for all points but choose a set of points with a certain criteria to fill up the Hough transform.
The consequence is that sometimes, you could miss the actual line if there wasnt eenough points ot start with. I guess you'd want to use this if you have somewhat linear structures so that most points would be redundant. reference no 2: L. Xu, E. Oja, and P. Kultanan, "A new curve detection method: Randomized Hough transform (RHT)", Pattern Recog. Lett. 11, 1990, 331-338.
I also read about some pretty different approaches where the algorithms would take two points and compute the point in the middle of those two points. if the point is an edge point, then we'd accumulate the bin for that line. This is apparently extremely fast but you'd assume a somewhat non-sparse matrix as you could easily miss lines if there wasnt enough edge points to start with.
回答2:
The OpenCV documentation states that the algoithm is based on "Robust detection of lines using the progressive probabilistic hough transform", by J Matas et al. This is quite different from the RHT described on wikipedia.
The paper does not seem to be freely available on the internet, but you can purcahse it from Elsevier
回答3:
The source code for HoughLinesProbabilistic in OpenCV 2.4.4 contains inline comments that explain the various steps involved.
https://github.com/Itseez/opencv/blob/master/modules/imgproc/src/hough.cpp
回答4:
The article Line Detection by Hough transformation in the section 6 could be useful.
回答5:
Here is a fairly concise paper by Matas et.al. that describes the approach, and as others mentioned, it is indeed quite different from Randomized Hough Transform:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.2186&rep=rep1&type=pdf
(Not sure for how long this link is going to be valid though. It's on/from citeseer, wouldn't expect it to just vanish tomorrow, but who knows...)
I had quick look at the implementation icvHoughLinesProbabilistic() in hough.cpp, because I'll be using it :-) It seems fairly straightforward, anyway, my primary interest was whether it does some least squares line-fitting in the end - it doesn't, which is fine. It just means, if it is desired to get accurate line-segments, one may want to use the start/end-point and implied line-parameters as returned by OpenCV to select related points from the overall point-set. I'd be using a fairly conservative distance-threshold in the first place, and run RANSAC/MSAC on these points with a smaller threshold. Finally, fit a line to the inlier-set as usual, e.g. using OpenCV's cvFitLine().
来源：https://stackoverflow.com/questions/4310411/what-is-the-particular-implementation-of-probabilistic-hough-transform-in-opencv