Line Segment Detector vs Probabalistic Hough Transform

Question

In OpenCV, there are two methods of detecting lines that give similar results in the form of a vector of endpoints - the Line Segments Detector (LSD) and the Probabilistic Hough Transform. (Discounting the standard Hough transform as the output given is in terms of equations, not line endpoints.)

I haven't been able to find a compare and contrast of these two line detection methods and their pros/cons. Thus - what is the difference between these two functions? Are there any particular benefits to using one method as opposed to the other?

Additionally, are there other lesser-known line detection methods (like LSD) that might be advantageous in some use cases?

Answer 1

Line Segments Detector (LSD)

• Takes a greyscale image as input
• Designed to work without any parameter tuning
• Runs in linear time
• Gives subpixel accurate results
• Useful for parameter-free edge detection
• LSD paper by Grompone Von Gioi et al
• Example implementation: using LSD to detect data matrices

(Progressive) Probabilistic Hough Transform

• Takes a binary image as input
• Has several tuning parameters; distance resolution (rho), angle resolution (theta), an accumulator threshold parameter (only those with enough votes are returned), minimum line length and maximum line gap
• Time performance depends on parameters (but is improved over standard Hough transform)
• Several runs may yield different results due to randomised nature
• Useful for more specific line finding; parameters allow for tuning, and has option to combine segments (via maximum line gap parameter) to return single longer lines
• The OpenCV implementation is the Progressive Probabilistic Hough Transform (with thanks to Dr. D.'s answer on this question)

Other algorithms

• EDLines: a linear time line segment detector that utilises an edge detector. No OpenCV implementation as far as I know.

(With thanks to Micka's comment for pointing out the differences in input and potential uses)