Editing Edge detection (section)

=== Edge thinning ===

Edge thinning is a technique used to remove the unwanted spurious points on the edges in an image. This technique is employed after the image has been filtered for noise (using median, Gaussian filter etc.), the edge operator has been applied (like the ones described above, Canny or Sobel) to detect the edges and after the edges have been smoothed using an appropriate threshold value.
This removes all the unwanted points, and if applied carefully, results in one pixel thick edge elements.

Advantages:
# Sharp and thin edges lead to greater efficiency in [[object recognition]].
# If [[Hough transform]]s are used to detect lines and ellipses, then thinning could give much better results.
# If the edge happens to be the boundary of a region, then thinning could easily give the image parameters like perimeter without much algebra.

There are many popular algorithms used to do this, one such is described below:

# Choose a type of [[Pixel connectivity#2-dimensional|connectivity]], like 8, 6 or 4.
# [[Moore neighborhood|8 connectivity]] is preferred, where all the immediate pixels surrounding a particular pixel are considered.
# Remove points from North, south, east and west.
# Do this in multiple passes, i.e. after the north pass, use the same semi processed image in the other passes and so on.
# Remove a point if:<br />The point has no neighbors in the North (if you are in the north pass, and respective directions for other passes).<br />The point is not the end of a line.<br />The point is isolated.<br />Removing the points will not cause to disconnect its neighbors in any way.
# Otherwise, keep the point.
The number of passes across direction should be chosen according to the level of accuracy desired.