hough-transformopencv
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
An alternative approach would be to extract features (keypoints) using the scale-invariant feature transform (SIFT) or Speeded Up Robust Features (SURF).
You can find a nice OpenCV code example in Java, C++, and Python on this page: Features2D + Homography to find a known object
Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle occlusion (as long as enough keypoints are visible).
Image source: tutorial example
The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.
Line Segments Detector (LSD)
(Progressive) Probabilistic Hough Transform
Other algorithms
(With thanks to Micka's comment for pointing out the differences in input and potential uses)
Best Answer
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