A Hough transform algorithm with a 2D hypothesis testing kernel

It is advantageous to use edge orientation information from an edge detector when trying to find lines in an image using a Hough transform algorithm. Such methods have to balance between two competing problems: (1) to reduce interference between different line segments in the image; and (2) to allow for increased orientation uncertainties particularly near junctions of lines. To counter both these problems the authors show that a smooth voting kernel which is a function of differences both in orientation and distance from the line, can give superior results. They compare these results with the results obtained from using the standard Hough implementation as well as an implementation with a kernel which varies smoothly with separation but remains independent of differences in orientation.<
>